Empirical Likelihood Semiparametric Regression Analysis under Random Censorship

By Qihua Wang, Gang Li. 2002

68 citations recorded by the source corpus.

This paper considers large sample inference for the regression parameter in a partly linear model for right censored data. We introduce an estimated empirical likelihood for the regression parameter and show that its limiting distribution is a mixture of central chi-squared distributions. A Monte Carlo method is proposed to approximate the limiting distribution. This enables one to make empirical likelihood-based inference for the regression parameter. We also develop an adjusted empirical likelihood method which only appeals to standard chi-square tables. Finite sample performance of the proposed methods is illustrated in a simulation study.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6378ffeeb0d31af81c2fe01d966612cc21a0700d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

A review on empirical likelihood methods for regression (2009, background): “See, e.g., Qin and Jing (2001b) and Wang and Li (2002) for the analysis of the partial linear model.”
EMPIRICAL LIKELIHOOD REGRESSION ANALYSIS FOR RIGHT CENSORED DATA (2003, unknown): “…(Hollander, McKeague and Yang (1997), Li, Hollander, McKeague and Yang (1996), Adimari (1997), Li (1995b), Murphy (1995)), Li and Van Keilegom (2002), Wang and Li (2002), Wang and Jing (1999) and Wang and Wang (2001)), and confidence tubes for multiple quantile plots (Einmahl and McKeague (1999)).”
Estimation in partially linear models with missing responses at random (2007, unknown): “By Wang and Li [20] and conditions (c)(ii), (e) and (f), we have E ⎡⎣ 1 √ n n ∑ j=1 ∣∣∣∣ n ∑ i=1 nj (Ti)(Xi − g1(Ti)) ∣∣∣∣ ⎤⎦2 c n ∑ j=1 n ∑”