BOOK-CHAPTER

- Computation of Empirical Likelihood Ratio with Censored Data

Abstract

The empirical likelihood ratio method is a general nonparametric inference procedure that has many desirable properties.Recently, the procedure has been generalized to several settings including testing of weighted means with right censored data.However, the computation of the empirical likelihood ratio with censored data and other complex settings is often non-trivial.We propose to use a sequential quadratic programming (SQP) method to solve the computational problem.We introduce several auxiliary variables so that the computation of SQP is greatly simplified.Examples of the computation with null hypothesis concerning the weighted mean are presented for right and interval censored data.

Keywords:
Empirical likelihood Computer science Computation Statistics Econometrics Mathematics Algorithm

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
8
Refs
0.54
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability

Related Documents

JOURNAL ARTICLE

Computation of the empirical likelihood ratio from censored data

Kun ChenMai Zhou

Journal:   Journal of Statistical Computation and Simulation Year: 2007 Vol: 77 (12)Pages: 1033-1042
BOOK-CHAPTER

Empirical Likelihood with Censored Data

Mohamed BoukelouaAmor Keziou

Lecture notes in computer science Year: 2023 Pages: 125-135
JOURNAL ARTICLE

Empirical likelihood with twice censored data

Mohamed Boukeloua

Journal:   Communication in Statistics- Theory and Methods Year: 2025 Vol: 54 (18)Pages: 6081-6115
JOURNAL ARTICLE

Empirical Likelihood Ratio for Linear Transformation Models with Doubly Censored Data

Pao‐Sheng Shen

Journal:   Communications in Statistics - Simulation and Computation Year: 2011 Vol: 41 (4)Pages: 531-543
JOURNAL ARTICLE

Empirical Likelihood Ratio With Arbitrarily Censored/Truncated Data by EM Algorithm

Mai Zhou

Journal:   Journal of Computational and Graphical Statistics Year: 2005 Vol: 14 (3)Pages: 643-656
© 2026 ScienceGate Book Chapters — All rights reserved.