Statistical Inference Based on Kernel Distribution Function Estimators is popular PDF and ePub book, written by Rizky Reza Fauzi in 2023-05-31, it is a fantastic choice for those who relish reading online the Mathematics genre. Let's immerse ourselves in this engaging Mathematics book by exploring the summary and details provided below. Remember, Statistical Inference Based on Kernel Distribution Function Estimators can be Read Online from any device for your convenience.
Statistical Inference Based on Kernel Distribution Function Estimators Book PDF Summary
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.
Detail Book of Statistical Inference Based on Kernel Distribution Function Estimators PDF
- Author : Rizky Reza Fauzi
- Release : 31 May 2023
- Publisher : Springer Nature
- ISBN : 9789819918621
- Genre : Mathematics
- Total Page : 103 pages
- Language : English
- PDF File Size : 11,9 Mb
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