Testing conditional independence with data missing at random

DOI10.1007/s11766-018-3527-7zbMath1413.62055OpenAlexW2890732743WikidataQ129278291 ScholiaQ129278291MaRDI QIDQ1989863

Yi Liu, Xiao Hui Liu

Publication date: 29 October 2018

Published in: Applied Mathematics. Series B (English Edition) (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11766-018-3527-7

zbMATH Keywords

missing at randomconditional independenceinverse probability weightingre-samplingcumulative sum process of residuals

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09)

Cites Work

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• A consistent characteristic function-based test for conditional independence
• Testing conditional independence using maximal nonlinear conditional correlation
• Model checking for partially linear models with missing responses at random
• Testing conditional independence via Rosenblatt transforms
• Empirical likelihood-based inference under imputation for missing response data
• Checking the adequacy of a general linear model with responses missing at random
• Integrated conditional moment test for partially linear single index models incorporating dimension-reduction
• Nonparametric tests for conditional independence using conditional distributions
• Model-Checking Techniques Based on Cumulative Residuals
• Fusion-Refinement Procedure for Dimension Reduction With Missing Response at Random
• Semiparametric dimension reduction estimation for mean response with missing data
• A NONPARAMETRIC HELLINGER METRIC TEST FOR CONDITIONAL INDEPENDENCE
• Asymptotic Statistics
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Test the reliability of doubly robust estimation with missing response data
• A Generalization of Sampling Without Replacement From a Finite Universe

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