Using the dependent wild bootstrap for the nonparametric goodness-of-fit test for density functions

DOI10.1080/02331888.2016.1153096zbMath1356.62060OpenAlexW2317545202MaRDI QIDQ2953562

Jeongcheol Ha, Cheolyong Park, Tae Yoon Kim, Zhi-Ming Luo, Sun Young Hwang

Publication date: 4 January 2017

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2016.1153096

zbMATH Keywords

bootstrapdependent wild bootstrapnonparametric density testsize and power distortions

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09) Functional limit theorems; invariance principles (60F17)

Cites Work

• Dependent wild bootstrap for degenerate \(U\)- and \(V\)-statistics
• Central limit theorem for quadratic errors of Nadaraya-Watson regression estimator under dependence
• The effect of dependence on chi-squared and empiric distribution tests of fit
• The power and optimal kernel of the Bickel-Rosenblatt test for goodness of fit
• Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates
• The central limit theorem for degenerate variableU-statistics under dependence
• Nonparametric testing the similarity of two unknown density functions: local power and bootstrap analysis
• On goodness-of-fit tests for weakly dependent processes using kernel method
• Nonparametric testing of closeness between two unknown distribution functions
• Bootstrapping a consistent nonparametric goodness-of-fit test
• ADAPTIVE TESTING IN CONTINUOUS-TIME DIFFUSION MODELS

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