ft makes the new yorker, october 4, 2010 page 71
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FT makes the New Yorker, October 4, 2010 page 71. The Fourier transform. regularity conditions Functions, A( ), - < < |A( )|d finite FT. a(t) = exp{it )A( )d - < < Inverse A( ) =(2 ) -1 exp{-i t} a(t) dt - PowerPoint PPT PresentationTRANSCRIPT
FT makes the New Yorker, October 4, 2010 page 71
The Fourier transform. regularity conditions
Functions, A(), - < <
|A()|d finite
FT. a(t) = exp{it)A()d - < <
Inverse A() =(2)-1 exp{-it} a(t) dt
unique
C()= A() + B()
c(t) = c(t) + b(t)
2 1
Convolution (filtering).
d(t) = b(t-s) c( s)ds
D() = B()C()
Discrete FT.
a(t) = T-1 exp{i2ts/T} A(2s/T) s, t = 0,1,...,T-1
A(2s/T) = exp {-i2st/T) a(t)
FFTs exist
Dirac delta.
g() () d = g(0)
exp {it}() d = 1
inverse
() = (2)-1 exp {-it}dt
Heavyside function H() = signum ()
() = dH()/d
Mixing. Stationary case unless otherwise indicated
cov{dN(t+u),dN(t)} small for large |u|
|pNN(u) - pNpN| small for large |u|
hNN(u) = pNN(u)/pN ~ pN for large |u|
qNN(u) = pNN(u) - pNpN u 0
|qNN(u)|du <
cov{dN(t+u),dN(t)}= [(u)pN + qNN(u)]dtdu
Power spectral density. frequency-side, , vs. time-side, t
/2 : frequency (cycles/unit time)
fNN() = (2)-1 exp{-iu}cov{dN(t+u),dN(t)}/dt
= (2)-1 exp{-iu}[(u)pN+qNN(u)]du
= (2)-1pN + (2)-1 exp{-iu}qNN(u)]du
Non-negative, symmetric
Approach unifies analyses of processes of widely varying types
Examples.
Spectral representation. stationary increments - Kolmogorov
)(}exp{/)(
)(1}exp{)(
N
N
dZitdttdN
dZiittN
})(){(},cov{ increments orthogonal
)()()}(),(cov{order of spectrumcumulant
...),...,()...()}(),...,({)()}({
)()(dZ valued,-complex random, :
111...11
N
YX
NNNN
KKNNKKNN
NN
NN
YXEYX
ddfdZdZK
ddfdZdZcumdpdZE
dZZ
Filtering.
dN(t)/dt = a(t-v)dM(v) = a(t-j )
= exp{it}A()dZM()
with
a(t) = (2)-1 exp{it}A()d
dZN() = A() dZM()
fNN() = |A()|2 fMM()
Association. Measuring? Due to chance?
Are two processes associated? Eg. t.s. and p.p.
How strongly?
Can one predict one from the other?
Some characteristics of dependence:
E(XY) E(X) E(Y)
E(Y|X) = g(X)
X = g (), Y = h(), r.v.
f (x,y) f (x) f(y)
corr(X,Y) 0
Bivariate point process case.
Two types of points (j ,k)
Crossintensity. a rate
Prob{dN(t)=1|dM(s)=1}
=(pMN(t,s)/pM(s))dt
Cross-covariance density.
cov{dM(s),dN(t)}
= qMN(s,t)dsdt no () often
Spectral representation approach.
b.v. of ,)()()}(),(cov{
)(}exp{/)(
)(}exp{/)(
NMMNNM
N
M
FddFdZdZ
dZitdttdN
dZitdttdM
Frequency domain approach. Coherency, coherence
Cross-spectrum.
duuquif MNMN )(}exp{21)(
Coherency.
R MN() = f MN()/{f MM() f NN()}
complex-valued, 0 if denominator 0
Coherence
|R MN()|2 = |f MN()| 2 /{f MM() f NN()|
|R MN()|2 1, c.p. multiple R2
where
A() = exp{-iu}a(u)du
fOO () is a minimum at A() = fNM()fMM()-1
Minimum: (1 - |RMN()|2 )fNN()
0 |R MN()|2 1
AAfAfAfff MMNMMNNNOO
Proof. Filtering. M = {j }
a(t-v)dM(v) = a(t-j )
Consider
dO(t) = dN(t) - a(t-v)dM(v)dt, (stationary increments)
Proof.
0 Take
0
sderivative second andfirst Consider
1
1
MNMMNMNN
MMNM
OO
MMNMMNNNOO
ffffffA
f
AAfAfAfff
Coherence, measure of the linear time invariant association of the components of a stationary bivariate process.
Regression analysis/system identification.
dZN() = A() dZM() + error()
A() = exp{-iu}a(u)du
Empirical examples.
sea hare
Mississippi river flow
Partial coherency. Trivariate process {M,N,O}
]}||1][||1{[/][ 22| ONMOONMOMNOMN ffffff
“Removes” the linear time invariant effects of O from M and N
Time series variants.
details later
continuous time case
Mixing.
cov{Y(t+u),Y(t)} = cYY(u)
small for large |u|
|cYY(u)|du <
Power spectral density. frequency-side, , vs. time-side, t
/2 : frequency (cycles/unit time)
fYY() = (2)-1 exp{-iu}cov{Y(t+u),Y(t)}
= (2)-1 exp{-iu}cYY(u)du -<<
Non-negative, symmetric
Approach unifies analyses of processes of widely varying types
Things in the frequency domain look the same
Spectral representation.
Y(t) = exp{it}dZY() - < t <
ZY() random, complex-valued conj{ZY()} = ZY(-)
E{dZY()} = ()cNd
cov{dZY(),dZY()}=(-)f NN()dd
cum{dZY(1),...,dZY(K)} = ...
Filtering.
Yt) = a(t-v)X(v)dv
= exp{it}A()dZX()
with
a(t) = (2)-1 exp{it}A()d
dZY() = A() dZX()
fYY() = |A()|2 fXX()
Bivariate time series case.
(X(t),Y(t)) - < t <
Cross-covariance function. general case
cov{X(s),Y(t)}
= cXY(s,t)
Spectral representation approach.
b.v. of ,)()()}(),(cov{
)(}exp{/)(
)(}exp{/)(
NMXYYX
Y
X
FddFdZdZ
dZitdttdY
dZitdttdX
FXY(.): cross-spectral measure
Frequency domain approach. Coherency, coherence
Cross-spectrum.
f XY()= (2)-1 exp{-iu)c XY(u)du -< <
complex-valued
Coherency.
R XY() = f XY()/{f XX() f YY()}
0 if denominator 0
Coherence.
|RXY()|2 = |f XY()| 2 /{fXX() fYY()|
|RXY()|2 1, c.p. multiple R2
Regression analysis/system identification.
dZY() = A() dZX() + error()
A() = exp{-iu}a(u)du
Partial coherency. Trivariate process {X,Y,O}
]}||1][||1{[/][ 22
| OYXOOYXOXYOXYffffff
“Removes” the linear time invariant effects of O from X and Y