# IS vs. self-normalised IS

**Xi'an's Og » R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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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…)

**M**aybe 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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