Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm

	Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm
Journal of Data Science and Modeling
مقاله 4، دوره 1، شماره 1، اسفند 2022، صفحه 33-44 اصل مقاله (446.13 K)
نوع مقاله: Research Manuscript
شناسه دیجیتال (DOI): 10.22054/jcsm.2018.9248
نویسنده
Soroush Pakniat^*
M.Sc. graduate, Allameh Tabataba'i University, Tehran, Iran.
چکیده
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.
کلیدواژه‌ها
nonparametric topological data analysis؛ persistence landscape؛ persistence homology؛ bootstrap method؛ density estimation

آمار تعداد مشاهده مقاله: 421 تعداد دریافت فایل اصل مقاله: 853