Presentation on theme: "Hypothesis testing and confidence intervals by resampling by J. Kárász."— Presentation transcript:
Hypothesis testing and confidence intervals by resampling by J. Kárász
Contents The bootstrap method The bootstrap analysis of the Kolmogorov- Smirnov test The bootstrap analysis of the GEV parameter investing The bootstrap analysis of the movig window method
Testing of homogenity Kolmogorov-Smirnov test Investigating the GEV parameter of dependence on time
Bootstrap method conditions of use is a random sample from the unknown probability distribution function (F) with finite variance. is the unknown parameter, the function of F. is the non-parametric estimate of the parameter, the function of the random sample.
Bootstrap method bootstrap estimate of the standard error is the standard error of the estimate. Then the bootstrap estimate is Unfortunatly in most cases it’s impossible to express it as a simple function of or the random sample, so we have to use numeric approximation.
Bootstrap method bootstrap sample and bootstrap replicate To approximate the empirical distribution function, the bootstrap algorithm takes random samples from the empirical distribution function: where This is the bootstrap sample. It is nothing else but a random sample from with replacement. By evaluating the statistic of interest we get a bootstrap replicate:
Bootstrap method approximation with Monte Carlo method 1.Independently draw a large number of bootstrap samples: 2.Evaluate the statistic of interest, so we get B bootstrap replicates: 3.Calculate the sample mean and sample standard deviation of the replicates:
Bootstrap method confidence intervals The histogram of the bootstrap replicates is an empirical density function for, so the and histogram percentiles are suitable limit estimates for the percent confidence interval.
Bootstrap method hypotesis testing H 0 : are iid random variables. H 1 : are not iid random variables. The bootstrap samples are drawn from the same distribution ( ) independently, so if H 0 holds, then and the replicates are from quite similar distribution, because F and are similar for large sample size. If is out of the empirical confidence interval,we accept H 1, in other case, we accept H 0.
Kolmogorov-Smirnov test H 0 : are iid random variables. H 1 : are not iid random variables. Our suppose was that the annual maximum water levels are independent, so if we refuse H 0 we have to accept that are not from the same distribution. This can even mean trend.
Results 1 means H 0 was refused. t=1,2,3 : parameter for cutpoint in K-S test.
Results - example Annual maximum water level at Szolnok, t=1
Results - example Annual maximum water level at Szolnok, t=2
Examinating the GEV parameter of shape H 0 : are iid random variables. H 1 : are not iid random variables. Our suppose is that the annual maximum water levels are independent, so if we refuse H 0 we have to accept that are not from the same distribution. This can even mean trend.
Results 1 means H 0 was refused. No dataset was found to refuse H 0 both in K-S test and GEV parameter testing.
Results – data table
Results - example
Further questions – two-peeked bootstrap empirical distributions
Permutation testing similar to bootstrap method Same as the bootstrap algorithm except that permutation sample is drawn without replacement. The hypotesis testing is similar too, we examine the estimate and the empirical confidence interval.
Moving window method forecast analysis by permutation method Our aims were: Simulating the original dataset by permutation. Supervise the quality of the forecast.