JOURNAL ARTICLE

Empirical likelihood for the response mean of generalized linear models with missing at random responses

Xiaohui Liu

Year: 2014 Journal:   Communications in Statistics - Simulation and Computation Vol: 46 (1)Pages: 164-173   Publisher: Taylor & Francis

Abstract

ABSTRACTIn this article, we present a new empirical likelihood ratio for constructing the confidence interval of the response mean of generalized linear models with missing at random responses. Compared with the existing methods, the proposal can avoid the so-called “curse of dimensionality” problem when the dimension of covariates is high, and is still chi-squared distributed asymptotically, nevertheless. Simulation studies are also provided to illustrate the performance of the developed method.KEYWORDS: Empirical likelihoodGeneralized linear modelsMissing at randomResponse meanMATHEMATICS SUBJECT CLASSIFICATION: 62H1262G20

Keywords:
Curse of dimensionality Covariate Mathematics Confidence interval Generalized linear model Statistics Empirical likelihood Linear model Dimension (graph theory) Random effects model Generalized linear mixed model Missing data Dimensionality reduction Econometrics Computer science Artificial intelligence Meta-analysis Medicine Combinatorics

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Topics

Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability
Statistical Methods and Bayesian Inference
Physical Sciences →  Mathematics →  Statistics and Probability
Advanced Statistical Methods and Models
Physical Sciences →  Mathematics →  Statistics and Probability

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