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portada Statistical Inference Based on Kernel Distribution Function Estimators
Type
Physical Book
Author
Publisher
Language
English
Pages
96
Format
Paperback
Dimensions
23.4x15.6x0.6 cm
Weight
0.16 kg.
ISBN13
9789819918614
Categories

Statistical Inference Based on Kernel Distribution Function Estimators

Rizky Reza Fauzi (Author) · Yoshihiko Maesono (Author) · Springer · Paperback

Statistical Inference Based on Kernel Distribution Function Estimators - Fauzi, Rizky Reza ; Maesono, Yoshihiko

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Synopsis "Statistical Inference Based on Kernel Distribution Function Estimators"

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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