Quantile regression and variable selection for partially linear single-index models with missing censoring indicators

Yuye Zou; Guoliang Fan; Riquan Zhang

doi:10.1016/j.jspi.2019.04.008

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Quantile regression and variable selection for partially linear single-index models with missing censoring indicators

Yuye Zou Guoliang Fan Riquan Zhang

Year: 2019 Journal: Journal of Statistical Planning and Inference Vol: 204 Pages: 80-95 Publisher: Elsevier BV

DOI: 10.1016/j.jspi.2019.04.008

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Keywords:

Censoring (clinical trials) Mathematics Estimator Inverse probability weighting Imputation (statistics) Quantile Feature selection Statistics Quantile regression Missing data Lasso (programming language) Accelerated failure time model Linear regression Covariate Computer science Artificial intelligence

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Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Advanced Causal Inference Techniques

Physical Sciences → Mathematics → Statistics and Probability

Spatial and Panel Data Analysis

Social Sciences → Economics, Econometrics and Finance → Economics and Econometrics

Quantile regression and variable selection for partially linear single-index models with missing censoring indicators

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