| Dewey Class |
519.5 |
| Title |
Statistical Inference Based on Kernel Distribution Function Estimators (EBook /) / by Rizky Reza Fauzi, Yoshihiko Maesono. |
| Author |
Fauzi, Rizky Reza |
| Added Personal Name |
Maesono, Yoshihiko |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2023. |
| Publication |
Singapore : : Springer Nature Singapore : : Imprint: Springer, , 2023. |
| Physical Details |
VIII, 96 p. 15 illus., 1 illus. in color. : online resource. |
| Series |
JSS Research Series in Statistics 2364-0065 |
| ISBN |
9789819918621 |
| Summary Note |
This book presents a study of statistical inferences based on the kernel-type estimators of distribution functions. The inferences involve matters such as quantile estimation, nonparametric tests, and mean residual life expectation, to name just some. Convergence rates for the kernel estimators of density functions are slower than ordinary parametric estimators, which have root-n consistency. If the appropriate kernel function is used, the kernel estimators of the distribution functions recover the root-n consistency, and the inferences based on kernel distribution estimators have root-n consistency. Further, the kernel-type estimator produces smooth estimation results. The estimators based on the empirical distribution function have discrete distribution, and the normal approximation cannot be improved—that is, the validity of the Edgeworth expansion cannot be proved. If the support of the population density function is bounded, there is a boundary problem, namely the estimator does not have consistency near the boundary. The book also contains a study of the mean squared errors of the estimators and the Edgeworth expansion for quantile estimators.: |
| Contents note |
Kernel density estimator -- Kernel distribution estimator -- Quantile estimation -- Nonparametric tests -- Mean residual life estimator. |
| Mode of acces to digital resource |
Digital reproduction.- |
| Cham : |
| Springer International Publishing, |
| 2023. - |
| Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-981-99-1862-1 |
| Links to Related Works |
Subject References:
Authors:
Corporate Authors:
Series:
Classification:
|
|---|
