ArticleJournal of applied statistics2024
Robust estimation and bias-corrected empirical likelihood in generalized linear models with right censored data.
Article in Journal of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Robust learning for ridge-penalized quasi-GLMs under non-identical distributions.Scientific reports · 2025Article
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3 authors.
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Abstract
In this paper, we study the robust estimation and empirical likelihood for the regression parameter in generalized linear models with right censored data. A robust estimating equation is proposed to estimate the regression parameter, and the resulting estimator has consistent and asymptotic normality. A bias-corrected empirical log-likelihood ratio statistic of the regression parameter is constructed, and it is shown that the statistic converges weakly to a standard
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