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I was grading my Master projects this morning and came upon this graph:

which compares the variability of an importance-sampling estimator versus its self-normalised alternative… This is an interesting case in that self-normalisation does considerably degrade the quality of the approximation in that setting. In other cases, self-normalisation may bring a clear improvement. (This reminded me of a recent email from David Einstein complaining about imprecisions in the importance section of Monte Carlo Statistical methods , incl. the fact that self-normalisation was not truly addressing the infinite variance issue. His criticism is appropriate, we should rewrite this section towards more precise statements…)

Maybe this is to be expected. Here is a similar comparison for finite and infinite variance cases:

compar=function(df,N){
y=matrix(rt(df=df,n=N*100),nrow=100)
t=sqrt(abs(y))*dcauchy(y)/dt(y,df=df)
w=dcauchy(y)/dt(y,df=df)
tone=t(apply(t,1,cumsum)/(1:N))
wone=t(apply(t,1,cumsum)/apply(w,1,cumsum))
dim(tone)
ttwo=apply(tone,2,max)
wtwo=apply(wone,2,max)
three=apply(tone,2,min)
whree=apply(wone,2,min)
plot(apply(tone,2,mean),col="white",ylim=c(min(three),max(ttwo)))
if (diff(range(tone[,100]))
The outcome is shown above, with an increased variability in the finite variance case (df=.5, left) and a (meaningful?) decrease in the infinite variance case (df=2.5, right).
Filed under: Books, R, Statistics, University life Tagged: importance sampling, infinite variance estimators, Monte Carlo Statistical Methods, R, self-normalised importance sampling

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