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Statistical inference based on kerne...

Fauzi, Rizky Reza.

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Statistical inference based on kernel distribution function estimators

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical inference based on kernel distribution function estimators/ by Rizky Reza Fauzi, Yoshihiko Maesono.
作者:	Fauzi, Rizky Reza.
其他作者:	Maesono, Yoshihiko.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	1 online resource (viii, 96 p.) :ill., digital ;24 cm.
內容註:	Kernel density estimator -- Kernel distribution estimator -- Quantile estimation -- Nonparametric tests -- Mean residual life estimator.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics. -
電子資源:	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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