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.
Pakniat,S . (2022). Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm. Journal of Data Science and Modeling, 1(1), 33-44. doi: 10.22054/jcsm.2018.9248
MLA
Pakniat,S . "Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm", Journal of Data Science and Modeling, 1, 1, 2022, 33-44. doi: 10.22054/jcsm.2018.9248
HARVARD
Pakniat S. (2022). 'Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm', Journal of Data Science and Modeling, 1(1), pp. 33-44. doi: 10.22054/jcsm.2018.9248
CHICAGO
S Pakniat, "Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm," Journal of Data Science and Modeling, 1 1 (2022): 33-44, doi: 10.22054/jcsm.2018.9248
VANCOUVER
Pakniat S. Statistical Topology Using the Nonparametric Density Estimation and Bootstrap Algorithm. JDSM. 2022;1(1):33-44. doi: 10.22054/jcsm.2018.9248
