Titlebook: Statistical Inference Based on Kernel Distribution Function Estimators; Rizky Reza Fauzi,Yoshihiko Maesono Book 2023 The Editor(s) (if app

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書目名稱Statistical Inference Based on Kernel Distribution Function Estimators讀者反饋學科排名

隱士

Kernel-Based Nonparametric Tests,ions of their test statistics converge to the same distributions as their unsmoothed counterpart, their improvement in minimizing errors can be proven. Some simulation results illustrating the estimator and the tests’ performances will be presented in the last part of this article.

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Adulterate

Kernel Distribution Function Estimator,r and its properties, a method to reduce the mean integrated squared error for kernel distribution function estimators is also proposed. It can be shown that the asymptotic bias of the proposed method is considerably smaller in the sense of convergence rate than that of the standard one, and even th

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amyloid

Mean Residual Life Estimator,l density estimation, eliminating the boundary bias problems that occur in the naive kernel estimator of the mean residual life function is needed. Here, the property of bijective transformation is once again utilized to define two boundary-free kernel-based mean residual life function estimators. F

Hemiparesis

Kernel-Based Nonparametric Tests,nown statistical tests are introduced. The three tests consist of Kolmogorov-Smirnov, Cramér-von Mises, and Wilcoxon signed test. Though the distributions of their test statistics converge to the same distributions as their unsmoothed counterpart, their improvement in minimizing errors can be proven

散布

Kernel Distribution Function Estimator,e variance of the proposed method is smaller up?to some constants. The idea of this method is using a self-elimination technique between two standard kernel distribution function estimators with different bandwidths, with some helps of exponential and logarithmic expansions. As a result, the mean squared error can be reduced.

Ambiguous

Kernel Quantile Estimation, is nonstandard since the influence function of the resulting .-statistic explicitly depends on the sample size. We obtain the expansion, justify its validity and demonstrate the numerical gains in using it.