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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 | ||
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آمار تعداد مشاهده مقاله: 398 تعداد دریافت فایل اصل مقاله: 813 |
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