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Variable screening with missing covariates: a discussion of ‘statistical inferencefor nonignorable missing data problems: a selective review’ by NianshengTang and Yuanyuan Ju

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Statistical Theory and Related Fields 2018年第2期2卷 134-136页

作者： Fang Fang Lyu Ni School of Statistics East China Normal University

Feature screening with missing data is a critical problem but has not been well addressed in theliterature. In this discussion we propose a new screening index based on “information value” andapply it to feature scr... 详细信息

关键词： Feature screening missing at random missing covariates

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The Horvitz-Thompson Weighting Method for Quantile Regression Estimation in the Presence of missing covariates

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Journal of Mathematical Research with Applications 2021年第3期41卷 303-322页

作者： Zhaoji CHU Lingnan TAI Wei XIONG Xu GUO Maozai TIAN Center for Applied Statistics School of Statistics Renmin University of China School of Statistics University of International Business and Economics School of Statistics Beijing Normal University Department of Medical Engineering and Technology Xinjiang Medical University School of Statistics and Information Xinjiang University of Finance and Economics

The lack of covariate data is one of the hotspots of modern statistical analysis. It often appears in surveys or interviews, and becomes more complex in the presence of heavy tailed, skewed, and heteroscedastic data. In this sense, a robust quantile regression method is more concerned. This paper presents an inverse weighted quantile regression method to explore the relationship between response and covariates. This method has several advantages over the naive estimator. On the one hand, it uses all available data and the missing covariates are allowed to be heavily correlated with the response; on the other hand, the estimator is uniform and asymptotically normal at all quantile levels. The effectiveness of this method is verified by simulation. Finally, in order to illustrate the effectiveness of this method, we extend it to the more general case, multivariate case and nonparametric case.

关键词： Robust quantile regression missing covariates selection probability Kernel estimator weighting method

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An embedded estimating equation for the additive risk model with biased-sampling data

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Science China Mathematics 2018年第8期61卷 1495-1518页

作者： Feipeng Zhang Xingqiu Zhao Yong Zhou School of Mathematics and Statistics Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education)Hunan Normal University Department of Applied Mathematics The Hong Kong Polytechnic University School of Statistics East China Normal University Academy of Mathematics and Systems Science Chinese Academy of Sciences

This paper presents a novel class of semiparametric estimating functions for the additive model with right-censored data that are obtained from general biased-sampling. The new estimator can be obtained using a weighted estimating equation for the covariate coefficients, by embedding the biased-sampling data into left-truncated and right-censored data. The asymptotic properties(consistency and asymptotic normality) of the proposed estimator are derived via the modern empirical processes theory. Based on the cumulative residual processes, we also propose graphical and numerical methods to assess the adequacy of the additive risk *** good finite-sample performance of the proposed estimator is demonstrated by simulation studies and two applications of real datasets.

关键词： additive risk model biased-sampling data missing covariates estimating equation model checking

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