projectors and linear estimation in general linear models

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This article was downloaded by: [Tufts University] On: 02 December 2014, At: 10:35 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Communications in Statistics - Theory and Methods Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/lsta20 Projectors and linear estimation in general linear models Radostaw Kala a a Department of Mathematical and Statistical Methods , Academy of Agriculture , Poznań, 60-637, Poland Published online: 27 Jun 2007. To cite this article: Radostaw Kala (1981) Projectors and linear estimation in general linear models, Communications in Statistics - Theory and Methods, 10:9, 849-873, DOI: 10.1080/03610928108828078 To link to this article: http://dx.doi.org/10.1080/03610928108828078 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

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Page 1: Projectors and linear estimation in general linear models

This article was downloaded by: [Tufts University]On: 02 December 2014, At: 10:35Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Communications in Statistics - Theory andMethodsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/lsta20

Projectors and linear estimation in general linearmodelsRadostaw Kala aa Department of Mathematical and Statistical Methods , Academy of Agriculture ,Poznań, 60-637, PolandPublished online: 27 Jun 2007.

To cite this article: Radostaw Kala (1981) Projectors and linear estimation in general linear models, Communicationsin Statistics - Theory and Methods, 10:9, 849-873, DOI: 10.1080/03610928108828078

To link to this article: http://dx.doi.org/10.1080/03610928108828078

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”)contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensorsmake no representations or warranties whatsoever as to the accuracy, completeness, or suitabilityfor any purpose of the Content. Any opinions and views expressed in this publication are the opinionsand views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy ofthe Content should not be relied upon and should be independently verified with primary sources ofinformation. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands,costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly orindirectly in connection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial orsystematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distributionin any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found athttp://www.tandfonline.com/page/terms-and-conditions

Page 2: Projectors and linear estimation in general linear models

COMMUN. STATIST.-THEOR. METII., A10(9), 849-873 (1981)

PROJECTORS AND LINEAR ESTIMATION I N GENERAL LINEAR MODELS

Department o f Ma themat i ca l and S t a t i s t i c a l Methods Academy o f A g r i c u l t u r e , 60-637 Poznai5, Po land

Key b?ords and Phrases: general Gauss-Markov model; minimm disper7sion linear unbiased estimator; simple least squares estimator.

ABSTRACT

The paper g i v e s a s e l f - c o n t a i n e d accoun t o f minimum d i s p e r -

s i o n l i n e a r unb iased e s t i m a t i o n o f t h e e x p e c t a t i o n v e c t o r i n a

l i n e a r model w i t h t h e d i s p e r s i o n m a t r i x b e l o n g i n g t o some, r a t h e r

a r b i t r a r y , s e t o f nonnega t i ve d e f i n i t e m a t r i c e s . The approach t o

l i n e a r e s t i m a t i o n i n genera l l i n e a r models recommended h e r e i s a

d i r e c t g e n e r a l i z a t i o n o f some ideas and r e s u l t s p r e s e n t e d by Rao

( 1 9 7 3 , 1 9 7 4 ) f o r t h e case o f a genera l Gauss-Markov model.

A new i n s i g h t i n t o t h e n a t u r e o f some e s t i m a t i o n prob lems

o r i g i n a l y a r i s i n g i n t h e c o n t e x t o f a genera l Gauss-Markov model

as we1 l as t h e cor respondence o f r e s u l t s known i n t h e 1 i t e r a t u r e

t o those o b t a i n e d i n t h e p r e s e n t paper f o r genera l l i n e a r models

a r e a l s o g i v e n . As p r e l i m i n a r y r e s u l t s t h e t h e o r y o f p r o j e c t o r s

d e f i n e d by Rao ( 1 9 7 3 ) i s ex tended.

1 . INTRODUCTION AND SUMMARY

The t h e o r y o f l i n e a r e s t i m a t i o n i n l i n e a r models has been

t r e a t e d i n t h e l i t e r a t u r e by u s i n g v a r i o u s methods. The deve lop -

Copyright O 198 1 by Marcel Dekker, Inc.

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KALA

ment o f the methods s t a r t s f rom the most e lementary techniques,

reviewed r e c e n t l y by Kempthorne (1976) , and con t inues through the

v a r i o u s a l g e b r a i c a l methods proposed by Goldman and Zelen (19641,

Zysk ind and M a r t i n (1969) and Rao and M i t r a (1971) , u n t i l t he

f u l l y geometr ica l methods i n i t i a t e d by Kruskal (1961) . I t seems,

however, t h a t the most a p p r o p r i a t e method i s t h a t which b r i n g s t o -

ge ther the s i m p l i c i t y o f e lementary c o n s i d e r a t i o n s , the p r a c t i c a l

usefu lness o f a l g e b r a i c a l s o l u t i o n s and the elegance o f the geo-

m e t r i c a l approach. For the case o f a general Gauss-Markov model,

such a p r e s e n t a t i o n o f e s t i m a t i o n theory has been ob ta ined by Rao

(1973, 19741, who used s u i t a b l y d e f i n e d p r o j e c t o r s as a main t o o l .

The o b j e c t o f the p resen t paper i s t o g i v e a s e l f - c o n t a i n e d

account o f the geomet r i ca l method o f l i n e a r e s t i m a t i o n o f the ex-

p e c t a t i o n v e c t o r i n a l i n e a r model w i t h the d i s p e r s i o n m a t r i x be-

long ing t o some, r a t h e r a r b i t r a r y , s e t o f nonnegat ive d e f i n i t e ma-

t r i c e s , and thus t o extend t o a w ider c l a s s o f l i n e a r models the

approach proposed by Rao.

Sec t ion 2 i s devoted t o an ex tens ion o f the theory o f p r o j e c -

t o r s in t roduced by Rao (1973) . Sec t ion 3 comprises the main re -

s u l t s on e s t i m a t i o n o f the e x p e c t a t i o n v e c t o r i n a general Gauss

-Markov model expressed, f o l l o w i n g Rao ( 1973, 1 9 7 4 ) ~ i n the lan -

guage o f p r o j e c t o r s . I n Sec t ion 4 and 5 t h i s geometr ica l approach

i s adopted i n d e r i v i n g a necessary and s u f f i c i e n t c o n d i t i o n f o r

the ex is tence o f the minimum d i s p e r s i o n l i n e a r unbiased e s t i m a t o r

o f the e x p e c t a t i o n v e c t o r i n a genera l l i n e a r model and i n charac-

t e r i z i n g the c l a s s o f a l l p r o j e c t o r s lead ing t o such an e s t i m a t o r ,

i f i t e x i s t s i n the model. Moreover, i n Sec t ion 5, the correspon-

dence i s shown between t h e r e s u l t s ob ta ined i n the p resen t paper

and those known i n the l i t e r a t u r e . The l a s t s e c t i o n , Sec t ion 6,

g i ves a new i n s i g h t i n t o the na tu re o f s o l u t i o n s o f th ree est ima-

t i o n problems f r e q u e n t l y a r i s i n g i n the c o n t e x t o f a genera l Gauss

-Markov model, one o f which i s d iscussed i n t h e l i t e r a t u r e s i n c e

1948.

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS

2. PROJECTORS AND THEIR PROPERTIES

I n t h i s s e c t i o n we extend the theory o f l i n e a r t rans fo rm-

a t i o n s c a l l e d p r o j e c t o r s , which appears t o be very u s e f u l i n s o l v -

i n g the e s t i m a t i o n problems i n l i n e a r models. The a t t r a c t i v e n e s s

o f such t rans fo rmat ions f o r the l i n e a r s t a t i s t i c a l i n f e r e n c e has

been p o i n t e d o u t by Rao ( 1 9 7 4 ) , who expressed the b a s i c r e s u l t s o f

the l i n e a r e s t i m a t i o n i n a Gauss-Markov model i n the e legan t geo-

m e t r i c a l language o f p r o j e c t o r s .

I n the sequel we wi 1 l say t h a t X ( A ) and X ( B ) , the column

space o f the nxp m a t r i x A and the nxq m a t r i x B , r e s p e c t i v e l y , a r e

d i s j o i n t subspaces o f Rn, t h e n-d iment ional Eucl idean space, i f

t h e i r i n t e r s e c t i o n i s the n u l l v e c t o r , i .e . , i f

Moreover, we w i l l say t h a t X ( A ) and X ( B ) are complementary i f they

a re d i s j o i n t and t h e column space o f the p a r t i t i o n m a t r i x ( A : B )

co inc ides w i t h Rn, i .e. , i f

X ( A ) n X ( B ) = { O ) and X ( A : B ) = Rn.

Now we i n t r o d u c e the n o t i o n o f a p r o j e c t o r , as considered by

Rao ( 1 9 7 3 ) .

Def i n i t i on 2 . 1 . Let X( A ) and X ( B ) be d i s j o i n t subspaces o f Rn

and l e t for every x E X ( A : B ) ,

represents the unique decomposition, such t h a t y E X ( A ) and z E

R( B ) . Then y i s sa id t o be t he pro jec t ion o f x onto X ( A ) along

X( B ) , and a matr ix P which transforms any z E X ( A : B ) i n t o i t s

p ro j ec t i on y f X( A ) i s sa id t o be a pro jec tor onto R ( A ) along X ( B ) .

v The d i f f e r e n c e between a p r o j e c t o r descr ibed i n D e f i n i t i o n

2 . 1 and t h a t considered i n the l i t e r a t u r e ( e . g . i n Ben- Israel and

G r e v i l l e , 1 9 7 4 , p. 50) l i e s i n the assumption on the subspaces

X ( A ) and X ( B ) . I n our case these subspaces a r e d i s j o i n t , w h i l e

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852 KALA

cus tomar i l y they a re assumed t o be complementary. The weakening

o f t h i s assumption causes (see Rao, 1574) t h a t , i n genera l , such

an extended p r o j e c t o r need no t be unique o r idempotent. Neverthe-

less , i t can be s imply c h a r a c t e r i z e d as a s o l u t i o n o f a m a t r i x

equat ion, as shown i n the f o l l o w i n g l e m a , due t o Rao ( 1 9 7 3 ) .

Lemma 2 . 1 . Let R( A ) and R( B ) be d isc jo in t subspaces o f Rn.

Then a matrix P i s a projector onto X(A) along R ( s ) i f and only

i s PA = A , PB = O . ( 2 . 1 )

Proo f . Since X ( A ) and R ( R ) a r e d i s j o i n t , any vec to r x €

X(A:h) can be decomposed un ique ly as x

z = Bb f o r some vec to rs a and b. Then,

P i s a p r o j e c t o r on to X(A) a long X ( B ) i

Aa f o r a l l vec to rs a and b . Th is r e l a t i '

t o the equa t ion ( 2 . 1 ) , which completes

= y + z, where y = Aa and

by D e f i n i t i o n 2 . 1 , a m a t r i x

f and o n l y i f P ( A a + Bb) =

on, however, i s e q u i v a l e n t

the p r o o f . V

The s e t o f a l l s o l u t i o n s o f

symbol { P A i B } , i .e.,

{PA,,} = {P: PA = A , PB = 0)

The correspondence between the se

( 2 . 1 ) wi l l be denoted by the

j e c t o r s on to R ( A ) a long R ( B ) i s g iven i n the nex t lemma

with the s e t o f a l l projecto

Proo f . The s e t { P A I B } i s

( 2 . 1 ) has a s o l u t i o n , f o r wh

t h a t

Lemma 2 . 2 . The s e t {PA I B } i s ncnerpty i f and only i f the

s-&spaces X( A ) and R( B) are d i s j o i n t , i n which case it coincides

r s onto R ( A ) along JlZ(B).

nonempty i f and o n l y i f the equa t ion

i c h i t i s necessary and s u f f i c i e n t

where pr imes denote transposes o f mat r i ces . The i n c l u s i o n (2.31,

however, i s e q u i v a l e n t t o the statement t h a t f o r any v e c t o r a and

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS

b such t h a t Aa + Bb = 0 t h e r e i s a l s o Aa = 0. Since t h i s s t a t e -

ment i s a c t u a l l y the necessary and s u f f i c i e n t c o n d i t i o n f o r d i s -

j o i n t e n e s s o f B ( A ) and X ( U ) , the f i r s t p a r t o f t h e lemma i s es-

t a b l i s h e d . For the r e s t o f the p r o o f we observe t h a t t h e second

p a r t o f the lemma f o l l o w s d i r e c t l y f rom Lemma 2 .1 . V

An e x p l i c i t e r e p r e s e n t a t i o n o f elements o f the s e t ( ' L I B "

o r e q u i v a l e n t l y o f the s e t o f a l l p r o j e c t o r s o n t o X ( A ) a long

X ( E ) , revea ls the lemma below. I n what f o l l o w s we use A- and A~

t o des igna te , r e s p e c t i v e l y , a g - inverse o f A and a m a t r i x o f

maximum rank such t h a t L'AL = P .

Lemma 2 . 3 . I f X ( A ) and K( B 1 ure d is , jo in t subspaces o.f Rn,

then P (1 { P A , Y} if and only if

P = A ( S ~ ' A ) - & ' + u ( A : B ) ' ' ( 2 . 4 )

Proo f . The r e s u l t f o l l o w s f rom Lemma 2 . 6 o f Rao ( 1 9 7 4 ) and

the o b s e r v a t i o n t h a t the second term on t h e r i g h t hand s i d e i n

' 2 . 4 i s a genera l s o l u t i o n o f the equa t ion ?(AL:?) = 0, w i t h r e -

spect t o T. 0

I n view o f the r e p r e s e n t a t i o n ( 2 . 4 ) i t i s easy t o c o n f i r m

the f a c t , mentioned a f t e r D e f i n i t i o n 2 . 1 , t h a t a p r o j e c t o r here

considered i s n e i t h e r un ique nor idempotent. However, i n t h e case

o f complementary subspaces X ( A ) and X ( B ) t h e f o l l o w i n g w e l l known

p r o p e r t i e s ho ld .

Lemma 2 .4 . I f jll( A ) and JR( B ) are complementary subspaces o f

Rn, then

( a ) there e x i s t s e xac t l y one projec tor onto IR(A) along X( B);

t h i s p r ~ ~ j e c t o r w i Z 2 be denoted by P A I R , ( b ) p A I H = A ( B ~ ' A ) - B ~ ' , ( c ) PA is idempotent.

Proo f . ( a ) and ( c j f o l l o w f rom Theorem 8 o f Ben- Is rae l and

G r e v i l l e (1974, p. 5 0 ) . ( b ) i s a consequence o f formula ( 2 . 4 ) and

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the observa t ion t h a t i f X ( A ) and X ( B ) a r e complementary, then

( A : B ) ~ = 0 . v

The nex t lemma prov ides the c o n d i t i o n under which t h e i n c l u -

s i o n r e l a t i o n between two se ts o f p r o j e c t o r s can be e s t a b l i s h e d .

Lemma 2 . 5 . L e t X ( C ) and X ( D ) be d i s j o i n t subspaces of Rn.

Then

Proo f . I f ( 2 . 5 ) ho lds , then, on account o f Lemma 2 .3 and 2.1,

every p r o j e c t o r

s a t i s f i e s a l s o ( 2 . 1 ) . S u b s t i t u t i n g ( 2 . 7 ) , w i t h U = 0 , i n t o the

f i r s t equa t ion i n ( 2 . 1 ) g i v e s c ( D ~ ' c ) - D ~ ' A = A , and t h e r e f o r e

X ( A ) - c R ( c ) .

On the o t h e r hand, s u b s t i t u t i n g ( 2 . 7 ) i n t o the second equa-

t i o n i n ( 2 . 1 ) g i v e s

Since U i s an a r b i t r a r y m a t r i x , the l a s t e q u a l i t y i m p l i e s , i n

p a r t i c u l a r , t h a t

and t h a t

From ( 2 . 9 ) i t f o l l o w s t h a t B has a r e p r e s e n t a t i o n B = CR + D S ,

f o r some mat r i ces R and S, which combined w i t h ( 2 . 8 ) and t h e f a c t

D ~ ~ D = O g i v e s

Therefore, the i n c l u s i o n X ( B ) c X ( D ) i s e s t a b l i s h e d . -

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS

Conversely, l e t (2.6) h o l d and l e t P E {PCID}, which se t i s

nonempty, s ince R(C) and R(D) a r e d i s j o i n t subspaces. Then, f o r

some mat r i ces R and S, A = CR, B = DS and, i n v iew o f Lemma 2.1,

PC = C (2.10)

and

PD = 0. (2.11)

Thus, p o s t m u l t i p l y i n g (2.10) by R and (2.11) by S i m p l i e s PA = A

and PB = 0 , which, by Lemma 2 . 1 again, i s e q u i v a l e n t t o the s t a t e -

ment t h a t P E {PAIB}. V

I t i s o f some i n t e r e s t t o no te t h a t t h e assumption on t h e

d i s j o i n t e n e s s o f subspaces R(C) and X(D) i s n o t necessary t o

show the i m p l i c a t i o n from (2.6) t o (2.5). T h i s i s a consequence

o f the f a c t t h a t i f R(C) f! X(D) # {0} then, on account o f Lemma

2 . 2 , {PCID} i s an empty se t , and thus, the r e l a t i o n (2.5) o b v i -

o u s l y ho lds .

Using the p reced ing lemma and the remark above, we can now

prove the f o l l o w i n g r e s u l t .

Lemma 2 .6 . Let A be an nxp matr ix and for r = I , . . . , t Zet

B, be an nxq, matr ix . Then t

{'AI(B~: ... : B ~ ) } s r 2 1 { P ~ ~ ~ , } y (2.12)

and i f X( A ) and X( B1 : . . . : Bt ) are d i s j o i n t subspaces o f Rn, then t n_ {PAIB,J # 0. r-1

Proo f . I n v iew o f the obv ious i n c

r = I , ..., t, i t f o l l o w s f rom Lemma 2

p r o o f o f i t t h a t

{'AI(R~:.. . : B ~ ) } 2 { P ~ ~ ~ , } ' =

us ions X((B,) c X ( B 1 : ... :Bt), 5 and t h e remark below the

T h i s e s t a b l i s h e s (2.12). To p rove (2.13) i t su

t h a t , i f subspaces X(A) and N(B1: . . . :Bt) a r e d

Lemma 2.2, the s e t on the l e f t hand s i d e i n (2

f f i ces t o observe

i s j o i n t , then, by

.12) i s nonempty.

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85 6

The nex t resu

se ts o f p r o j e c t o r s

Lemma 2.7 . Le

KALA

I t i s a l s o concerned w i t h t h e i n t e r s e c t i o n o f

t A be an nxp matrix and f i r r = I , . . . , t l e t

B, and C , be, r e spec t i ue l y , t he nxq, and g,xs matr ices . Then

Proof . I f the se t on the l e f t hand s ide i n ( 2 . 1 4 ) i s an empty

s e t , then the i n c l u s i o n holds. Thus, we assume t h a t t h i s set i s

nonempty and t h a t P i s any o f i t s elements. Then, i t f o l l o w s from

Lemma 2.1 t h a t PA = A and P B , = 0 f o r r = 1 , . . . , t. The l a s t

equat ions imply P ( B I C 1 + ...+ B T C t ) = 0 , which combined w i t h

PA = A g ives

Th is e s t a b l i s h e s ( 2 . 1 4 1 , thus complet ing the p r o o f . V

An i n t e r e s t i n g s i m p l i f i c a t - i o n o f Lemma 2.7 can be ob ta ined i f

we l e t s = q , and q , = q f o r r = 1 , . . . , t , and a l s o l e t C, = a,I,

where a , represents a s c a l a r w h i l e I, the i d e n t i t y m a t r i x . A f t e r

such m o d i f i c a t i o n s the lemma above can be expressed as f o l l o w s .

Lemma 2.8. Let A be an nxp matr ix and for r = I , ..., t l e t

B, be an nxq matr ix . Then

for any scalars a l , ..., a t - V

For the end o f t h i s s e c t i o n we r e c a l l the n o t i o n o f the o r -

thogonal p r o j e c t o r and then we d e r i v e the w e l l known p r o p e r t i e s o f

such p r o j e c t o r s .

D e f i n i t i o n 2 .2 . Let R ( A ) be a subspace o f Rn. Then the pro-

jec tor onto X i A ) along J Z ( A ~ ) i s said t o be the orthogonal projec-

t o r onto R i A ) . V

The n o t i o n o f o r t h o g o n a l i t y used i n D e f i n i t i o n 2.2 i s l i n k e d

w i t h the concept o f o r t h o g o n a l i t y o f subspaces X ( A ) and x (A ' ) , which, i n our case, a r e or thogonal i n the usual sense o f the

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s t a n d a r d i n n e r p r o d u c t i n k n . The common p r o p e r t i e s o f t h e o r t h o -

gona l p r o j e c t o r s a r e c o l l e c t e d i n t h e f o l l o w i n g .

Lemma 2 . 9 . Let ? ; ( A ) be c subspace of Rn. Then

( u i there e x i s t s c r u c t l y one orthogonal pro jec tor o n t o ?(A!;

t h i s proj'eczor uiZ? be denoted by PA , ( s ) = A ( A I A ) - A I , ( C ) PA is i&yote l ; t an2 s y m e t i ~ i o .

P r o o f . ( a ! i s an immedia te consequence o f Lemma 2 . 4 ( a ) and

t h e f a c t t h a t R ( h ) and x ( A ~ ) a r e complementary subspaces o f R".

( b ) f o l l o w s fromi Lemma 2 . 4 ( b l and t h e o b s e r v a t i o n t h a t one p o s s i b l e

c h o i c e c f ( h i ) i i s A i t s e l f . !c) i s easy t o v e r i f y u s i n g ( b ) and

t h e b a s i c p r o p e r t i e s o f g - i n v e r s e s . V

3. ESTIMATION OF THE EXPECTATION VECTOR IN GENERAL GAUSS-MARKOV MODELS

A genera l Gauss-Markov model i s o r d i n a r i l y denoted by t h e

t r i p l e t

where y i s an o b s e r v a b l e random ? - v e c t o r w i t h t h e e x p e c t a t i o n

E ( y ) = Yg and t h e d i s p e r s i o n m a t r i x D(: l ' = - . I n t h i s se t -up * i s

an q x p known m a t r i x o f a r b i t r a r y rank , [ i s a p - v e c t o r o f unknown

parameters and V i s an n x a nonnega t i ve d e f i n i t e m a t r i x known en-

t i r e l y o r e x c e p t f o r a p o s i t i v e s c a l a r m u l t i p l i e r .

One o f t h e main prob lems c o n s i d e r e d i n t h e l i t e r a t u r e devo ted

t o t h e t h e o r y o f Gauss-Markov model i s t h e p rob lem o f e s t i m a t i n g

l i n e a r l y t h e e x p e c t a t i o n o f p . I t s s o l u t i o n i s w e l l known n o t o n l y

f o r v a r i o u s p a r t i c u l a r cases o f t h e model d e s c r i b e d b u t a l s o f o r

t h e case o f i t s g e n e r a l f o r m u l a t i o n . D e s c r i p t i o n s o f t h e s o l u t i o n

can be found i n t h e s t a n d a r d t e x t s on s t a t i s t i c a l i n f e r e n c e ( s e e

e.g. A l b e r t , 1 9 7 2 ; Rao, 1 9 7 3 ~ ' . N e v e r t h e l e s s , we c o l l e c t he re main

r e s u l t s o f t h e t h e o r y , t r e a t i n g t h i s s e c t i o n as a base f o r f u r t h e r

genera l i z i n g c o n s i d e r a t i o n s , f o r wh ich t h e model {y, XP, V ) ap-

pea rs t o be a s p e c i a l case.

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D e f i n i t i o n 3 . 1 . In a genera2 Zinear Gauss-Markov mode2

{ y , X B , V ) a s t a t i s t i c Py, where P i s an nxn matr ix , i s cg22ed the

Minimwn Dispersion Linear Unbiased Estimator (MDLUE) o f X4 i f i t

i s unbiased .for XP and i f for any o ther Zinear unbiased es t imator

o f XP, say By,

D(By) - D(?I) 2 0,

the i nequa l i t y meaning t h a t the matr ix D(By) - D(P!y) i s nonnega-

t i v e d e f i n i t e . v

The key t o determine the c l a s s o f a l l t rans fo rmat ions P

l e a d i n g t o the MDLUE o f XP i s the c r i t e r i o n which appears i n the

paper by Seely and Zysk ind ( 1 9 7 1 ) , a t t r i b u t e d t o E . Lehmann and

H. Schef fe by t h e former au thors . Th is c r i t e r i o n can a l s o be found

i n the paper by Rao (19731, who, however, a t t r i b u t e s i t s o r i g i n t o

R . A. F i sher . We p resen t i t here w i t h o u t p r o o f .

Theorem 3.1 . I n a genera2 Gauss-Markov model { y , X B , V i , Pg

i s the MDLUE o f X B i f and only i f

I t should be observed t h a t the cons is tency o f ( 3 . 1 ) i s ac-

t u a l l y a necessary and s u f f i c i e n t c o n d i t i o n f o r the e x i s t e n c e o f

the MDLUE o f X p , which, i n view o f Lemma 2 . 2 , i s e q u i v a l e n t t o the

d i s j o i n t e n e s s o f subspaces X ( X ) and JK(vxL). I n the case o f the gen-

e r a l Gauss-Markov model { y , XP, V ) , however, t h e MDLUE o f X C- a l -

ways e x i s t s i n v iew o f the f o l l o w i n g lemma g iven by Rao (1974) .

Lemma 3 . 1 . Let X be an nxp matrix and l e t V be an nxn nomega-

t i v e d e f i n i t e matrix . Then X ( X ) and TZ( VX') are d i s j o i n t subspaces

o f Rn. v

Taking i n t o account the remark above and D e f i n i t i o n 2.1, we

can express the r e s u l t o f Theorem 3.1 as f o l l o w s .

Theorem 3.2. Let { y , X B , V ) be a genera2 Gauss-Markov model.

Then

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( a ) the MDLUE o f X4 e x i s t s ,

( b ) Py i s t he MDLUE o f XR i f and only i f P E {PXI v x ~ } . V

The genera l r e p r e s e n t a t i o n o f t rans fo rmat ions l e a d i n g t o the

MDLUE o f XP can now e a s i l y be d e r i v e d u s i n g Lemma 2.3, by which a

ge:leral form o f p r o j e c t o r s f rom t h e s e t {P I) i s o b t a i n a b l e . XI VX Never theless, we c h a r a c t e r i z e elements o f t h i s s e t w i t h the h e l p

o f Theorem 8(b) o f Rao and Yanai ( 1 9 7 9 ) ( s e e a l s o Rao, 1973, 1978),

which appears t o be more f r u i t f u l i n f u r t h e r c o n s i d e r a t i o n s .

Theorem 3 .3 . Let X be an nxp ma t r i x and l e t V be an nxn non-

negative d e f i n i t e matrix . Then t he general soZution o f the

eqilation

can be expressed as

P = x(x~T-x)-x~T- + A(I - TT-1,

where A i s an a rb i t ra ry matr ix , T = V + XUX' and U i s any symmetric

mat&x such t h a t R ( T ) = X(V:X). V

Al though Theorem 3 . 3 i s fo rmu la ted i n t h e sense o f g i v i n g a

genera l s o l u t i o n o f the m a t r i x equa t ion (3.2), i t f o l l o w s f rom

Lemma 2.2 and t h e d i s j o i n t e n e s s o f subspaces X(X) and XZ(vXL), t h a t

the theorem g ives a t t h e same t ime a genera l form o f a l l p r o j e c t o r s

o n t o R(X) a long X(VxL) and thus c h a r a c t e r i z e s the elements o f the

se t IPXIvx~J.

4 . ESTIMATION OF THE EXPECTATION VECTOR IN GENERAL LINEAR MODELS

A model, i n which t h e problem o f the l i n e a r e s t i m a t i o n o f t h e

e x p e c t a t i o n v e c t o r w i l l now be d iscussed, i s determined by t h e

usual moment represen ta t i o n

where y i s an observable random n - v e c t o r , X i s an nxp known m a t r i x ,

p i s a p - v e c t o r o f unknown parameters, V1, . . . , Vt a r e known non-

n e g a t i v e d e f i n i t e nxn m a t r i c e s and Al, ..., A t a r e unknown s c a l a r s .

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I t i s easy t o observe t h a t the d i f f e r e n c e between t h e general

Gauss-Markov model considered i n the p rev ious s e c t i o n and the

model desc r ibed by ( 4 . 1 ) l i e s i n the s t r u c t u r e o f the d i s p e r s i o n

m a t r i x D ( y ) , which ranges now over the set

D = { V : V = hlVl + . . . + htVt 2 0, h, E R} ( 4 . 2 )

o f nonnegative d e f i n i t e mat r i ces , r a t h e r than i s p r o p o r t i o n a l t o

a g iven m a t r i x V. I n the sequel the s e t o f c o n d i t i o n s ( 4 . 1 ) t o -

ge ther w i t h ( 4 . 2 ) w i l l be denoted by the t r i p l e t

{Y, XD, D}

and w i l l be c a l l e d a general l i n e a r model. To avo id redundant e l -

ements i n the s p e c i f i c a t i o n o f { y ,

t h a t the mat r i ces V 1 , . . ., Vt a r e

t h a t

dim {V: V = alVl + . . . + atVt

XD, D}, we assume moreover

i n e a r l y independent, i . e . ,

Under the genera l set -up descr ibed above we w i l l now be con-

cerned w i t h the two r e l a t e d problems d iscussed b r i e f l y i n t h e p r e -

v ious s e c t i o n f o r the case o f a general Gauss-Markov model. The

f i r s t o f them p e r t a i n s t o the ex is tence o f t h e MDLUE o f Xa, which

i s meant here i n accordance w i t h the f o l l o w i n g

Def i n i t i o n 4.1 . In a genera2 l i near mode2 { y , X P , D) a s t a t -

i s t i c Py, where P i s an nxn m a t ~ i x , i s ca22ed t he minimum disper-

s ion l i near unbiased est imator o f XR i f for any V E D FLJ i s t he

MDLUE o f X p i n the mode2 { y , X B , If}. V

The second problem can be fo rmu la ted as a genera l c h a r a c t e r -

i z a t i o n o f a l l t rans fo rmat ions lead ing t o the MDLUE o f X P , pro -

v ided such e s t i m a t o r i n { y , X P , D} e x i s t s .

For the f i r s t problem, i n c o n t r a r y t o t h e second, t h e r e a r e

a l r e a d y known s o l u t i o n s ob ta ined e i t h e r i n v a r i o u s p a r t i c u l a r

cases o f the 1 i n e a r model { y , X P , D) (see Eaton, 1970; Seely and

Zyskind, 1971; M i t r a and Moore, 1973) o r i n the genera l framework

o f the model (see M i t r a and Moore, 1976). Never the less, we so lve

i t again here, p r e s e n t i n g e s s e n t i a l y new geometr ica l approach

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS 86 1

which i s a n a t u r a l e x t e n s i o n o f the c o n s i d e r a t i o n s o f t h e p rev ious

s e c t i o n f o r the Gauss-Markov model. The second problem mentioned

above w i l l a l s o be d iscussed here.

Theorem 4.1. Let {y, X 8 D) be a genernl linear model. Then

(a) the MDLUE of X D exists if and only if t

C' WXlV & # @, r=l

( 4 . 3 )

! b ) if (a! is the case, Py 7:s the MDLUE of X D if an2 only if

p c n { p X l V Xl}. (4.4 r=1 l-

Proo f . Since f o r r = I, ..., t V, E G, the e x i s t e n c e o f the

MDLUE o f i? i n model {y, XO, D) i m p l i e s , i n v iew o f D e f i n i t i o n 4.

t h a t t h e r e e x i s t s a m a t r i x P such t h a t Py i s the MDLUE o f X E i n

each genera l Gauss-Markov model {y, XD, V,), f o r r = 1 , . .., t. Therefore, i t f o l l o w s f rom Theorem 3.2 t h a t

E { P , f o r r = 1 , . . . , t , r"

thus e s t a b l i s h i n g ( 4 . 4 ) and ( 4 . 3 ) .

To prove the converse i m p l i c a t i o n l e t assume ( 4 . 3 ) and l e t P

be any m a t r i x s a t i s f y i n g ( 4 . 4 ) . Then, from Lemma 2 .8 , w i t h A = X

and f o r r = I, . . . , t w i t h B, = Vrzi and a, = h,, i t f o l l o w s t h a t

which i s t r u e f o r any s e t o f s c a l a r s {Al, ..., A*}. I n consequence

P E { P X j m ~ ) f o r a l l V E D and thus, i n v iew o f Theorem 3.2 and

D e f i n i t i o n 4.1, % i s the MDLUE o f XO i n the genera l l i n e a r model

{y, XO, Dl. v

More p r e c i s e i n s p e c t i o n o f the

t h a t Theorem 4.1 e s t a b l i s h e s a c t u a l

t

"5, = ,$ {PXI V r X ~ ) ' VED

I t i s i n t e r e s t i n g t o no te t h a t

p r o o f above a l l o w s us t o s t a t e

l y the a q u a l i t y

(4.5)

nonemptiness o f t h e se t on t h e

l e f t hand s i d e i n ( 4 . 5 ) i s , by D e f i n i t i o n 4.1 and Theorem 3.2,

e q u i v a l e n t t o the e x i s t e n c e o f the MDLUE o f X[3 i n a genera l l i n e a r

model {y, XP, D), and, moreover, i f t h i s i s t h e case t h e s e t c o i n -

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c ides w i t h the s e t o f a l l p r o j e c t o r s , which a p p l i e d t o y g i v e the

requ i red MDLUE . Comparing Theorem 4.1 and 3.2 i t i s easy t o observe t h a t the

l a t e r f o l l o w s f rom t h e former by s e t t i n g t = I and us ing Lemma 3.1

toge ther w i t h Lemma 2.2 t o s i m p l i f y the statement ( a ) . On t h e

o t h e r hand, i n t h e case o f a Gauss-Markov model { y , XP, V) Theorem

3.2 g ives , by a p p l y i n g Lemma 2.3, a genera l form o f t rans fo rm-

a t i o n s p r o v i d i n g the MDLUE o f Xg, b u t i n t h e case o f t h e ex is tence

o f the MDLUE o f Xp i n a genera l l i n e a r model {y, XP, D} Theorem

4.1 mere ly shows t h a t a f u l l c h a r a c t e r i z a t i o n o f t rans fo rmat ions

lead ing t o t h e MDLUE o f X P i s g i ven by the se t o f p r o j e c t o r s t h a t

appears on t h e r i g h t hand s i d e i n ( 4 . 5 ) . Thus, the problem o f

f i n d i n g a genera l formula f o r the p r o j e c t o r s lead ing t o t h e MDLUE

i n a general l i n e a r model { y , XP, D), when t > I, i s no t answered

y e t . A l though the s o l u t i o n o f t h i s problem w i l l be g iven i n t h e

next s e c t i o n , we can now g i v e q u i c k answers f o r two s p e c i a l cases

o f t h e model {y, XP, Dl.

C o r o l l a r y 4.1. Let i n a l i near model { y , XB, D) t he MDLUE of

XP e x i s t and l e t V o E D be a pos i t i v e d e f i n i t e matr i z . Then the

s e t o f a22 transformations leading t o the MDLUE of XP contains

the unique projec tor PX , V O X ~ only . Proo f . I f Vo i s p o s i t i v e d e f i n i t e , then TUX) and X(V~X') a r c

complementary subspaces o f Rn and thus, by Lemma 2.4, t h e se t

{PXlvoXL} c o n s i s t s o f one element P X I V o X ~ o n l y . T h i s combined w i t h

the e q u a l i t y ( 4 . 5 ) and the f a c t s t h a t Vo E D and t h a t the MDLUE o f

X R e x i s t s , i m p l i e s t h e r e s u l t . V

The nex t c o r o l l a r y i s an immediate consequence o f C o r o l l a r y

4.1 and D e f i n i t i o n 2.2.

C o r o l l a r y 4.2. Let i n a Linear model { y , XB, 11) the MDLUE of

X B e x i s t and Zet I E D. Then the s e t o f a l l transformations lead-

ing t o the MDLUE o f X b contains the orthogonal pro jec tor PX only .

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5. PROJECTORS LEADING TO THE MDLUE OF X P

Before d e r i v i n g the general formula f o r t rans fo rmat ions an-

nounced i n t h e t i t l e , we g i v e t h r e e e q u i v a l e n t s o f the c o n d i t i o n

( 4 . 3 ) f o r the e x i s t e n c e o f the MDLUE o f XP. To t h i s end i t w i l l be

convenient t o i n t r o d u c e the f o l l o w i n g concept, which p res8mab ly

was f i r s t used i n t h e con tex t o f a l i n e a r model by LaMotte ( 1 9 7 7 ) .

D e f i n i t i o n 5 . 1 . A matr ix Va E D i s said t o be a

ement i n the s e t D if R( V ) g X I ( V:,:) for a l l I/ E Do

When 3 i s meant as the s e t desc r ibed by (4.2), i

observe t h a t one o f the mat r i ces f u l f i l l i n g the condi

n i t

I t

s e t

maximal e l -

L'

t i s easy t o

t i o n s o f D e f i -

on 5.1 i s the m a t r i x

V l + ... + V, . ( 5 . 1 )

s a l s o easy t o no te t h a t t o determine a maximal element i n t h e

D i t i s n o t , i n genera l , necessary t o use formula ( 5 . I ) , and

sometimes the maximal element can be found on a s imp le r way. For

instance, when U c o n t a i n s a p o s i t i v e d e f i n i t e m a t r i x , then t h i s

m a t r i x i s a maximal element i n D.

Now we a re i n a p o s i t i o n t o g i v e the e q u i v a l e n t s o f the con-

d i t i o n ( 4 . 3 ) .

Theorem 5.1. Let { y , XP, D) be a general l i near model, l e t V..

be a maximal element i n the s e t D and l e t

where U i s an arbi t rary symmetric matr ix such t h a t XI(T,) = X I ( V,,,:X).

Then the following statements are equivalen t :

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Proof . I f the c o n d i t i o n (5.3) i s s a t i s f i e d then, on account

o f Lemma 2 . 1 , t h e r e e x i s t s a m a t r i x Po such t h a t

POX = X (5.7)

and

P~V~X' = 0 , f o r r = I , . .., t. (5.8)

Since V* E D, Va = alV, + ... + a,Vt f o r some s c a l a r s al, ..., at 9

which combined w i t h (5.8) and (5.7) shows t h a t Po i s a s o l u t i o n o f

the equa t ion

On the o t h e r hand, i t f o l l o w s f rom Theorem 3 .3 , w i t h V rep laced by

V;:, t h a t a general s o l u t i o n o f (5.9) takes the form

where A i s an a r b i t r a r y m a t r i x and T:,: i s as de f ined i n (5.2).

Therefore, f o r some A0

x(x'T,x)-X'T~ + A ~ ( I - T:,:T;) = po . (5.10)

S u b s t i t u t i n g (5.10) i n t o (5.8) leads t o (5.41, s ince

- T?:T:-:V, = V , , f o r r = I , . . . , t,

and x'T~x(x'T~x)-X'T~ = X ' T ~ . Now assume (5.4). Then

f o r some m a t r i x R. P r e m u l t i p l y i n g (5.12) by T?: and us ing (5.11)

again, shows t h a t

m(v,xL: . . . :vtxL) - c X(T;;X').

Hence (5.5) f o l l o w s , as TStXL = V;:XL, which i s obv ious i n view o f

(5.2).

The nex t i m p l i c a t i o n can be e s t a b l i s h e d by observ ing t h a t ,

s ince V?: € D, V9: 2 0. Therefore, by Lemma 3 .1 , X(X) and R(V;.:XL)

are d i s j o i n t subspaces, and thus the c o n d i t i o n (5.5) i m p l i e s t h a t

R(X) and X ( V ~ X ~ : . . . : V ~ X ~ ) a r e a l s o d i s j o i n t . But t h i s i s a c t u a l l y

the statement (5.6).

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS 865

To comp le te t h e p r o o f i t s u f f i c e s t o n o t e t h a t t h e i m p l i c a -

t i o n f r o m ( 5 . 6 ) t o ( 5 . 3 ) i s an immediate consequence o f Lemma 2.6.

v From t h e above c o l l e c t i o n o f necessa ry and s u f f i c i e n t c o n d i -

t i o n s f o r t h e e x i s t e n c e o f t h e MDLUE o f t h e e x p e c t a t i o n v e c t o r i n

a genera l l i n e a r model {y, XE, TI) t h e c o n d i t i o n ( 5 . 4 ) i s a d i r e c t

g e n e r a l i z a t i o n o f t h e r e s u l t due t o M i t r a and Moore ( 1 9 7 6 ) . T h e i r

c o n d i t i o n f o l l o w s f r o m ( 5 . 4 ) by u s i n g V:.: = V , + . . . + I/, as a

maximal e lement i n D and s u b s t i t u t i n g i t i n Y?: = 7:: + Xu', wh ich

i s ( 5 . 2 ) w i t h U = I. The c o n d i t i o n ( 5 . 6 ) , i n t u r n , i s a s i m p l i f i -

c a t i o n o f t h e c r i t e r i o n deve loped by Drygas ( 1 9 7 2 ) .

The n e x t theorem e s t a b l i s h e s a base f o r d e r i v i n g a genera l

f o r m u l a f o r t r a n s f o r m a t i o n s l e a d i n g t o t h e MDLUE o f XC, i f i n t h e

model iy, Xi3, C) such e s t i m a t o r e x i s t s .

Theorem 5.2 . L e t {y, XB, D} b e n genera l l i n e a r nodcl on2 Let

V?: be u rrazirml e l e v e n t i n t h e s e t 3. Then eac4 of t h e ,corzdit.ions

from ( 5 .3) t o ( 5 . 6 ) .is 7:ecessni.y m i se i f f i c ien t f o r the eqmlitli

P r o o f . I n v iew o f Theorem 5.1 , i t s u f f i c e s t o show t h a t any

one o f t h e c o n d i t i o n s l i s t e d t h e r e i s a necessa ry and s u f f i c i e n t

f o r t h e e q u a l i t y ( 5 . 1 3 ) . I n t h e p r o o f we e x p l o i t t h e c o n d i t i o n

(5.5), w h i c h seems t o be t h e most o p e r a t i v e .

Now obse rve t h a t , on accoun t o f t h e o b v i o u s r e l a t i o n s

Lemma 2.5 t o g e t h e r w i t h t h e remark g i v e n a f t e r t h e p r o o f o f i t

i m p l y t h e i n c l u s i o n

Moreover , i t f o l l o w s . f rom Lemma 2.8 t h a t

t

{PXI V,XL} C V$;& ' r= l

s i n c e V s = a l V l + ... + ntVt f o r some s c a l a r s al, ..., at. On t h e o t h e r hand, s i n c e V;,: 2 0, t h e subspaces X(X) and

~ L ( v : , : x ~ ) a r e d i s j o i n t . Thus, u s i n g Lemma 2 . 5 , we can s t a t e t h a t

( 5 . 5 ) i s e q u i v a l e n t t o

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1 1, { p ~ ~ ~ f i ~ L J c { p ~ ~ ( ~ l ~ L : .. .:VtX ) (5.16)

which, i n view o f (5.14) and (5.15), completes the p r o o f . V

The r e s u l t o f Theorem 5.2 combined w i t h the statement ( b ) o f

Theorem k . 1 and Lemma 2.3 g ives , i n p r i n c i p l e , a d e f i n i t e answer

t o t h e f o l l o w i n g ques t ion : What i s a general representa t io , : o f

pro jec tors ieading t o the MDLUE of Xp i n a general l i near model

{y, Xp, D ) ? Never theless, i t seems t h a t the most e legan t form o f

such a r e p r e s e n t a t i o n can be ob ta ined by the use o f Theorem 3 .3 ,

thus lead ing t o a g e n e r a l i z a t i o n o f the r e s u l t o f Rao ( 1973) (see

h i s Corol l a r y 3 . 7 ) .

Theorem 5.3. Let { y , XP, D) be a general l i near model, l e t

be a maximal element i n t he s e t D and l e t Tk be a matr ix de f ined

i n (5.2). Then any one o f t h e condit ions from (5.3) t o (5.6) i s

necessary and s u f f i c i e n t for the ex i s t ence o f t he MDLUE o f Xp, and

i f it does e x i s t t he general representa t ion o f pro jec tors P provid-

i ng t he MDLUE o f XP i s

P = X(X'T~X)-X'T~ + A(I - T ~ ~ T ~ ) , (5.17)

where A i s an arbi t rary matr ix .

Proo f . I n view o f Theorem 4 . 1 , 5.1 and 5.2 we search f o r a

general form o f p r o j e c t o r s i n the s e t {PXI v.,.XL). By Lemma 2.1,

t h i s genera l r e p r e s e n t a t i o n i s e q u i v a l e n t t o a genera l s o l u t i o n o f

the equa t ion

PX = X , P V ~ ~ X ~ = 0. (5.18) . But, on account o f Theorem 3 . 3 , (5.17) i s j u s t such a s o l u t i o n ,

and t h i s concludes the p r o o f . V

I t should be emphasized t h a t t h e set { P X , v , X ~ J e x p l o i t e d i n

the p roo f above i s always nonempty, and t h a t i t co inc ides w i t h the

se t o f a l l p r o j e c t o r s lead ing t o the MDLUE o f XP i f and o n l y i f

any o f t h e c o n d i t i o n s l i s t e d i n Theorem 5 .1 i s s a t i s f i e d . However,

we can say more i n regard t o the se t {P xl(vlxL:. . .:vtx+ which,

by Theorem 4 .1 and 5.2, can a l s o be used t o c h a r a c t e r i z e t h e c l a s s

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS 867

o f a l l t r a n s f o r m a t i o n s p r o v i d i n g t o t h e MDLUE o f XP i n t h e genera l

l i n e a r model {y, Xb, D}. Namely, by t h e use o f Lemma 2.2 and i n

view o f the c o n d i t i o n ( 5 . 6 ) i t i s easy t o no te t h a t t h e se t

{l=z,(v,xL:.. .:v+xL) } i s nonempty i f and o n l y i f the MDLUE o f XP i n

the model { y , XP, D) e x i s t s . There fo re , nonemptiness o f the se t

I } i s i n f a c t an a l t e r n a t i v e c r i t e r i o n f o r t h e iPm ( vlxL: . . . : vtx ) e x i s t e n c e o f t h e MDLUE o f XR, w h i l e the same i s n o t t r u e i n t h e

case o f t h e s e t {PXI v , X ~ } .

Concluding t h i s s e c t i o n l e t us throw some

l a r i e s o f Sec t ion 4. As i t i s easy t o see, t h i

based on a common assumption t h a t D con ta ins a

element, which, i n view o f D e f i n i t i o n 5 .1 , imp

1 i ght on the c o r o

s c o r o l l a r i e s a r e

p o s i t i v e d e f i n i t e

l i e s t h a t a maxima

element i n D, V$:, i s a l s o p o s i t i v e d e f i n i t e . As t h e consequence,

R(X) and x(v:~:x~) a r e complementary subspaces, and thus, by Lemma

2.4, t h e r e e x i s t s t h e unique p r o j e c t o r o n t o R(X) a long R(v;:x~).

There fo re , t h e f o l l o w i n g r e s u l t i s e s t a b l i s h e d .

C o r o l l a r y 5.1. Let i n a general l i near model {y, Xp, D} the

MDLUE o f XP e x i s t and l e t a maximal element i n D, V* , be p o s i t i v e

d e f i n i t e . Then t he MDLUE o f X@ i s provided by t he unique projec tor

px I vaxL. v

The r e s u l t s o f C o r o l l a r y 4 .1 and 4.2 can now e a s i l y be ob-

t a i n e d from the c o r o l l a r y above. To t h i s end i t s u f f i c e s t o r e -

p lace Va by Vo o r by I, r e s p e c t i v e l y .

The c o n d i t i o n o f C o r o l l a r y 5.1 i s o n l y a s u f f i c i e n t c o n d i t i o n

f o r the uniqueness o f t h e p r o j e c t o r l e a d i n g t o t h e MDLUE o f XB, i f

the e s t i m a t o r i n t h e model { y , XP, D} e x i s t s . However, by t h e Note

1 . 1 o f Rao (1978), t h e equa t ion (5.18) and i t s genera l s o l u t i o n

(5.17), i t i s easy t o e s t a b l i s h a c o n d i t i o n t h a t i s n o t o n l y s u f f i -

c i e n t b u t a l s o necessary.

Corol l a r y 5.2. Let i n a general Linear model {y, XP, D} t he

MDLUE o f Xb e x i s t and Let be a maximal element i n t he s e t D.

Then a pro jec tor leading t o the MDLUE o f X B i s unique i f and onZy

i f JR(Va:X) = Rn. V

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KALA

6. THREE RELATED PROBLEMS

I n t h i s s e c t i o n we consider th ree e s t i m a t i o n problems t h a t ,

by t h e i r p r a c t i c a l importance, focus much a t t e n t i o n o f many

authors. These problems a r e here so lved by adop t ing the approach

o f the p rev ious s e c t i o n s , which a l s o g ives a new i n s i g h t i n t o t h e

na tu re o f the s o l u t i o n s ob ta ined .

The f i r s t problem can be s t a t e d b r i e f l y as f o l l o w s : f i a t are

necessary and s u f f i c i e n t condi t ions for the orthogonal projector

P t o provide, by pro jec t ing y, the MDLUE of X B i n a X -Markov model { y , X p, V } ?

The problem has a long h i s t o r y , presumably o r i g i

the paper by Anderson (19481, who n o t i c e d t h a t i f i n

model {y, Xb, V } the mat r i ces X and V a re o f f u l l c o l

genera2 Gauss

n a t i n g from

a l i n e a r

umn ranks and

the columns o f X are a l l or thogonal e igenvec to rs o f the m a t i x V,

then the Simple Least Squares Es t imato r (SLSE) o f Xp, i . e . P X g , i s

i d e n t i c a l w i t h the MDLUE o f XB. Anderson's s u f f i c i e n t c o n d i t i o n

was l a t e r red iscovered by Magness and McGuire ( 1 9 6 2 ) , who showed

t h a t i t i s b o t h necessary and s u f f i c i e n t f o r the e q u a l i t y between

the d i s p e r s i o n mat r i ces o f t h e SLSE o f Xp and o f the MDLUE o f XP,

which a c t u a l l y i s e q u i v a l e n t t o the e q u a l i t y between these two

s t a t i s t i c s themselves. Independently, Zysk ind ( 1 9 6 2 ) , ex tend ing

cons idera t ions t o a model {y, XP, V ) w i t h X o f no t f u l l column

rank, has g iven e i g h t e q u i v a l e n t necessary and s u f f i c i e n t cond i -

t i o n s . Another statement o f the " i f and o n l y i f " c o n d i t i o n f o r

the considered e q u a l i t y has been e s t a b l i s h e d by Rao (1967) . A l -

though Rao's r e s u l t has been proved under the assumption t h a t X

and V a r e o f f u l l column ranks, he has remarked on i t t h a t i t i s

a l s o v a l i d i n the case when these mat r i ces a r e o f a r b i t r a r y ranks.

T h i s f a c t has then been conf i rmed by Zysk ind (1967) (see a l s o Rao,

1968), who has extended t h e se t o f h i s e a r l i e r e i g h t c o n d i t i o n s

by d e l e t i n g t h e r e s t r i c t i v e rank assumption on t h e m a t r i x V, and

who a l s o d iscussed t h e equiva lence between h i s extended r e s u l t s

and the c o n d i t i o n due t o Rao (1967) . From the r e s u l t s o f Zysk ind

l e t us r e c a l l t h a t which seems t o be o f the s imp les t form.

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS

Theorem 6 . 1 . In n general Gums-Markov mode2 {y, Xi? , V} t h e

SLSE o f .YE coinc ides u i t h t he MDLUF of XP i f and o n l y i$

Proo f . F i r s t n o t e t h a t ( 6 . 1 ) i s e q u i v a l e n t t o

Now, f rom Lemma 2 . 5 , D e f i n i t i o n 2 . 2 and Lemma 2.9 , i t f o l i o w s t h a t

( 6 . 2 ) i s a necessary and s u f f i c i e n t c o n d i t i o n f o r t h e r e l a t i o n

P X t { P X I V X & ( 6 . 3 )

which, i n view o f Theorem 3 . 2 , completes the p r o o f . V

We remark now t h a t t h e c o n d i t i o n ( 6 . 3 ) can e q u i v a l e n t l y be

w r i t t e n i n t h e form

{pLly1? {F X I 7~ 11 # @, ( 6 . 4 )

which i s ve ry a t t r a c t i v e by i t s s i m i l a r i t y w i t h the c o n d i t i o n (4 .31

o f Theorem 4 . 1 . T h i s o b s e r v a t i o n leads immediate ly t o t h e conc lu -

s i o n t h a t t h e c o n d i t i o n ( 6 . 1 ) assures n o t o n l y e q u a l i t y between

the SLSE o f X B and the MDLUE o f Xp i n the model {y, X f , V}, b u t i t

a l s o assures t h e e x i s t e n c e o f t h e MDLUE o f XP i n t h e genera l

l i n e a r model { ~ g , XP, So}, w i t h

Co = i v : v = hlV + hZI 2 0, h l , E R!.

Moreover, s i n c e I t i t f o l l o w s f rom C o r o l l a r y 4 . 2 t h a t Px i s

the unique p r o j e c t o r lead ing t o t h e MDLUE o f XP i n t h i s genera l

l i n e a r model.

The second problem i s a s imp le g e n e r a l i z a t i o n o f t h e p r e v i o u s .

I t can be fo rmu la ted as f o l l o w s : idhat are necessary an? s u f f f c i m t

condi t ions for each projec tor leading t o t he MDLUC of XP i n the

Gauss-Markov modeZ { z j , XP, Vl} t o proz)ide a l so t h e MDLUE o f X3 7'n

t he Gauss-Markou mode2 { z j , XP, V2}?

The f i r s t a t tempt t o answer t h i s q u e s t i o n was made by Thomas

(1968) , who has g iven two necessary and s u f f i c i e n t c o n d i t i o n s , b u t

o n l y f o r the case when t h e d i s p e r s i o n mat r i ces i n b o t h models a r e

nons ingu la r . Th is a d d i t i o n a l requirement i s n o t assumed i n the

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KALA 870

cons i de

Moore (

r a t ions conducted by Rao ( 197

1973). From among v a r i o u s equ

I) and l a t e r

i v a l e n t cond

by M i t r a and

i t i o n s we r e c a l l

here the f o l l o w i n g due t o Rao (1971 ) .

Theorem 6.2 . Each projector leading t o t he MDLUE o f XB i n a

general Gauss-Markov model {y, XP, V1} provides a l so t he MDLUE o f

XP i n a Gauss-Markov model {y, XP, V2} i f and only i f

X( v2x1) 2 X( v1xL) . (6.5)

Proof . I t f o l l o w s f rom Lemma 2.5 t h a t (6.5) i s e q u i v a l e n t t o

iPx I v1xL} s. IPx I v2xL} (6.6)

On account o f Theorem 3.2, the r e l a t i o n (6.6) proves t h e r e s u l t .

v I t i s easy t o observe t h a t the c o n d i t i o n (6.6) can a l s o be

w r i t t e n i n the e q u i v a l e n t form

ipx, vlxL} "IPxl v2xL} = IPx, vlxl} I n view o f Lemma 3.1 and 2.1, the se t on the r i g h t hand s i d e i n

t h e above e q u a l i t y i s nonempty. Thus, w i t h the use o f Theorem 4.1,

we can s t a t e t h a t each p r o j e c t o r i n t h e s e t { P X l y l X ~ } prov ides

n o t o n l y the MDLUE o f X@ i n the model { y , XP, V 2 } but a l s o the

MDLUE o f XP i n the genera l l inear model {y, XP, D+}, where

I n the t h i r d problem we a r e a l s o concerned w i t h two d i f f e r e n t

Gauss-Markov models. The problem can be s t a t e d i n t h e f o l l o w i n g

form: What are necessary and s u f f i c i e n t condi t ions for t he e x i s t -

ence o f a s t a t i s t . 7 : ~ t h a t i s t he MDLUE o f Xi3 whichever o f t he models

iy, XB, Vli and {y, XD, 1/21 i s ac tua l l y t rue?

T h i s ques t ion was answered by Rao ( 1968). However, we r e c a l l

here a l a t e r r e s u l t o f the same au thor , Rao (1971), which seems t o

be much s imp le r than t h e o r i g i n a l s o l u t i o n .

Theorem 6.3. Let {y, XR, Vl 1 and {y, XB, V2] be two general

Gauss-Markov models. Then there e x i s t s a s t a t i s t i c t h a t i s the

MDLUE o f XP whichever model i s t rue i f and only i f

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LINEAR ESTIMATION IN GENERAL LINEAR MODELS 871

Ins tead o f showing the p r o o f , which can be e a s i l y e s t a b l i s h e d ,

we no te t h a t c o n d i t i o n ( 6 . 8 ) a c t u a l l y c o i n c i d e s w i t h the c o ~ d i t i o n

( 5 . 6 ) o f Theorem 5.1 when t = 2 . Thus, by the use o f t h e r e s u l t s

o f Theorem 5.1 and 4.1, we can a s c e r t a i n t h a t ( 6 . 8 ) assures n o t

o n l y the e x i s t e n c e o f a p r o j e c t o r lead ing t o the MDLUE o f Xi3 i n

bo th models, { y , X P , V1) and {y, Xlj, V 2 ) , b u t i t i s a l s o the

necessary and s u f f i c i e n t c o n d i t i o n f o r the e x i s t e n c e o f the MDLUE

o f X l j i n the genera l l i n e a r model { y , Xp, I l t } , where D i s the se t t d e f i n e d i n ( 6 . 7 ) . Moreover, Theorem 4 . 1 combined w i t h Theorem 5.2

a l l o w us t o s t a t e t h a t i f c o n d i t i o n ( 6 . 8 ) i s s a t i s f i e d , then the

se t {PXI(v,X~:v2X~)} con ta ins a l l p r o j e c t o r s p r o v i d i n g the MDLUE.

ACKNOWLEDGMENT

T h i s research was completed w h i l e the au thor was a t the

Mathematical l n s t i

The au thor i s ve ry

i n v i t a t i o n t o the

t u t e o f the P o l i s h Academy o f Sciences i n Warsow.

indebted t o Pro fessor C . Olech f o r h i s k i n d

I n s t i t u t e .

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Ben- Is rae l , A. and G r e v i l l e , T. N. E . ( 1 9 7 4 ) . Genera l i zed I n u e r s e s : Theor2y and A p p l i c a t i o n s . New York: John W i l ey .

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