Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm
Publish place: The Journal of Data Science and Modeling، Vol: 1، Issue: 1
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JCSM-1-1_004
تاریخ نمایه سازی: 18 فروردین 1400
Abstract:
This paper presents approximate confidence intervals for each function of parameters in a Banach space based on a bootstrap algorithm. We apply kernel density approach to estimate the persistence landscape. In addition, we evaluate the quality distribution function estimator of random variables using integrated mean square error (IMSE). The results of simulation studies show a significant improvement achieved by our approach compared to the standard version of confidence intervals algorithm. Finally, real data analysis shows that the accuracy of our method compared to that of previous works for computing the confidence interval.
Keywords:
nonparametric topological data analysis , persistence landscape , persistence homology , bootstrap method , density estimation