small area prediction under alternative model specifications by wayne a. fuller and andreea l....

21
Small Area Prediction under Alternative Model Specifications By Wayne A. Fuller and Andreea L. Erciulescu Department of Statistics, Iowa State University Small Area Estimation 2014 Poznan, Poland, September, 2014

Upload: leonard-watson

Post on 16-Dec-2015

213 views

Category:

Documents


0 download

TRANSCRIPT

Small Area Prediction under Alternative Model Specifications

By

Wayne A. Fuller and Andreea L. Erciulescu

Department of Statistics, Iowa State University

Small Area Estimation 2014

Poznan, Poland, September, 2014

2

Outline

I. Motivating example

II. Models: auxiliary information

III. Bootstrap for prediction MSE

IV. Simulation

Conservation Effects Assessment Project (CEAP): Natural Resources Conservation Service

Impacts of conservation practices

Sample of fields

Subsample: National Resources Inventory(NRI)

Hydrologic Units

3

4

Unit Level Model

5

n informatioAuxiliary

discrete and Continuous

0)},(|{

effect area

),0(~,),,(

,,2,1;,,2,1),(2

ij

iijij

i

biijiijij

iijij

y

beE

b

Nbebgy

njmixy

x

βx

Auxiliary Data

6

(NRI) practices Cover, :~(known) data Soils :

2

1

x

x

μ

μ

Parameters

7

})ˆ{()ˆ(

}ˆ),,(|{ˆ

)( of parameters

),,,(

}),,(|{~

)(),,(

2

22

iiii

iiii

xii

ieb

iiii

xii

x

i

EMSE

E

xF

E

xdFbg

ψxy

φ

φβψ

ψxy

βx

Parametric Bootstrap

8

*,

1

11

**

2*,

*,

*,

*,

*,

,1

,1

22

1

ˆ :one Level

)ˆ(

samplefor predicted ˆ

samplefor true

seed random

),ˆ( :generatorData

)ˆ,ˆ,ˆ,ˆ(ˆ :sample Original

ki

B

kii

kikiki

ki

ki

k

k

eb

B

k

k

r

rDG

ψ

φβψ

9

Double Bootstrap Estimation

**,,

11

12

11

***

**,

*,,

*,

**,

*,

**,,

1

12,

*

,,2*

2

,11

21

2

ˆ

sample one-levelfor estimate is ˆ

),ˆ( samples two-level generateeach,For

),ˆ( samples one-level Generate

tki

B

t

B

kii

kikikikiki

kitki

B

tki

k

tkk

k

BB

iasB

BiasB

k

rDGB

rDGB

ψ

ψ

ψ

10

Fast Double Bootstrap

*****,

****,

**1,,

1

11

***

*,

**1,,

:)(ˆ

each for one Generate

bias of est.an is

(2007)Kinnon Mac andDavidson

1

iiCi

iikiki

B

k

kk

kiki

BiasB

11

Telescoping Double Bootstrap

)2()1(

)()1(ˆ

),( uses sample twoLevel

),ˆ( uses sample one Level

**,

*,

1

1

11

**,

*1,

*,

1

1

11

**,

1,1*

,1

1

1

kiki

B

k

kikiki

B

kTi

kk

k

B

B

rDG

rDG

ψ

ψ

12

CEAP Simulation Model

)(),(

,, all of ind. ),0(~

),(~

),( Binomial~

)exp(1

)exp(),g(x

observed ),(,),(

2

2

10

10ij

xdFbxg

jtixNIb

NIx

bxgy

bx

bxb

xyebxgy

xii

x

i

tjbi

xiij

iijij

iij

iiji

ijijijiijij

13

Alternative Specifications for x

Some external information

Area means known

Estimated random means

No external information

Area means fixed

Area means random

14

Simulation Parameters

)36.0,16.0,25.0(),,(

)0.1,8.0,0.0(),,(

)exp(1

)exp(),(

),(}|{ bernoulli

each 12)40,10,2( areas; 36

222

10

10

10

b

x

ijixij

iij

iijiij

iijijijij

i

x

bx

bxbxg

bxgxyEy

n

15

Estimation and Prediction

}ˆ),~,,(|{ˆ

),,( and ),(for REML 222

ψxy

β

xiiiii

xb

E

16

KnownMSE tionMSE/PredicPrediction xiμ

Size

2 1.76 1.43 1.16

10 1.20 1.12 1.05

40 1.09 1.04 1.02

xi

xi

~ No

Fixed

xi

xi

~ No

Random

xi

xi

~

Random

17

)1,100(),( Samples, 400 21 BBMC

(%), MSE Prediction ofEstimator Bootstrap

2 Rel Bias -14.6 -9.4 -9.4

Rel Sd 38.9 45.1 44.7

10 Rel Bias -13.2 -6.8 -6.8

Rel Sd 30.7 36.5 35.9

40 Rel Bias -7.5 -1.9 -1.9

Rel Sd 20.1 23.3 25.1

***** ˆˆˆ Size CT xixi ~ Observed , Random

Equal Efficiency Bootstrap Samples

Random , Observed

18

Bootstrap Level One TotalTelescoping (100, 1) 100 200Classic (100, 1) 150 300Classic (44, 50) 44

2244

Summary

Fast double bootstrap improves bootstrap efficiency

Double bootstrap reduces bias (about 50%)

Double bootstrap increases variance (15 to 30 %)

Random x model has potential to reduce MSE

19

Future Work

Confidence Intervals

Triple Bootstrap

Regression with Bootstrap

Nonparametric Bootstrap

Predictions for CEAP

20

Thank You

21