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Statistical inference based on kerne...

Fauzi, Rizky Reza.

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Statistical inference based on kernel distribution function estimators

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical inference based on kernel distribution function estimators/ by Rizky Reza Fauzi, Yoshihiko Maesono.
Author:	Fauzi, Rizky Reza.
other author:	Maesono, Yoshihiko.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	1 online resource (viii, 96 p.) :ill., digital ;24 cm.
[NT 15003449]:	Kernel density estimator -- Kernel distribution estimator -- Quantile estimation -- Nonparametric tests -- Mean residual life estimator.
Contained By:	Springer Nature eBook
Subject:	Mathematical statistics. -
Online resource:	https://doi.org/10.1007/978-981-99-1862-1
ISBN:	9789819918621

Statistical inference based on kernel distribution function estimators
Fauzi, Rizky Reza.

Statistical inference based on kernel distribution function estimators[electronic resource] /by Rizky Reza Fauzi, Yoshihiko Maesono. - Singapore :Springer Nature Singapore :2023. - 1 online resource (viii, 96 p.) :ill., digital ;24 cm. - JSS research series in statistics,2364-0065. - JSS research series in statistics..

Kernel density estimator -- Kernel distribution estimator -- Quantile estimation -- Nonparametric tests -- Mean residual life estimator.

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.

ISBN: 9789819918621

Standard No.: 10.1007/978-981-99-1862-1doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Statistical inference based on kernel distribution function estimators
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245 1 0 $a Statistical inference based on kernel distribution function estimators $h [electronic resource] / $c by Rizky Reza Fauzi, Yoshihiko Maesono.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a 1 online resource (viii, 96 p.) : $b ill., digital ; $c 24 cm.
490 1 $a JSS research series in statistics, $x 2364-0065
505 0 $a Kernel density estimator -- Kernel distribution estimator -- Quantile estimation -- Nonparametric tests -- Mean residual life estimator.
520 $a 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.
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650 0 $a Kernel functions. $3 562986
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650 2 4 $a Mathematical Statistics. $3 3538791
700 1 $a Maesono, Yoshihiko. $3 3634551
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830 0 $a JSS research series in statistics. $3 3386949
856 4 0 $u https://doi.org/10.1007/978-981-99-1862-1
950 $a Mathematics and Statistics (SpringerNature-11649)