Empirical likelihood and variable selection for partially linear single-index EV models with missing censoring indicators

Yuye Zou; Guoliang Fan; Riquan Zhang

doi:10.1007/s42952-020-00065-6

ScienceGate Book Chapters

JOURNAL ARTICLE

Empirical likelihood and variable selection for partially linear single-index EV models with missing censoring indicators

Yuye Zou Guoliang Fan Riquan Zhang

Year: 2020 Journal: Journal of the Korean Statistical Society Vol: 50 (1)Pages: 134-162 Publisher: Elsevier BV

DOI: 10.1007/s42952-020-00065-6

Get Full-Text PDF Get Analytical Report

Keywords:

Mathematics Empirical likelihood Censoring (clinical trials) Statistics Estimator Covariate Missing data Imputation (statistics) Marginal likelihood Inference Econometrics Maximum likelihood Computer science

Metrics

Cited By

0.98

FWCI (Field Weighted Citation Impact)

Refs

0.75

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Advanced Causal Inference Techniques

Physical Sciences → Mathematics → Statistics and Probability

Empirical likelihood and variable selection for partially linear single-index EV models with missing censoring indicators

Metrics

Citation History

Topics

Related Documents

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

Empirical likelihood for partially linear single-index models with missing observations

Penalized Empirical Likelihood based Variable Selection for Partially Linear Quantile Regression Models with Missing Responses

Empirical likelihood in single-index partially functional linear model with missing observations

Empirical Likelihood for Generalized Partially Linear Single-index Models