Semiparametric Approach to a Random Effects Quantile Regression Model.

Journal of the American Statistical Association
Mi-Ok Kim, Yunwen Yang

Abstract

We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest, while the random coefficients represent cluster specific deviations in the covariate effects. We formulate the estimation of the random coefficients as an estimating equations problem and use empirical likelihood to incorporate the parametric likelihood of the random coefficients. A likelihood-like statistical criterion function is yield, which we show is asymptotically concave in a neighborhood of the true parameter value and motivates its maximizer as a natural estimator. We use Markov Chain Monte Carlo (MCMC) samplers in the Bayesian framework, and propose the resulting quasi-posterior mean as an estimator. We show that the proposed estimator of the population-level parameter is asymptotically normal and the estimators of the random coefficients are shrunk toward the population-level parameter in the first order asymptotic sense. The...Continue Reading

References

Aug 21, 2003·Biometrics·David B DunsonJack A Taylor
Apr 26, 2006·Biostatistics·Marco Geraci, Matteo Bottai
Nov 30, 2006·Drug and Alcohol Dependence·Dennis McCartyFrances Cotter
Mar 21, 2007·Joint Commission Journal on Quality and Patient Safety·Victor A CapocciaTodd Molfenter
Dec 2, 2009·Biostatistics·Brian J ReichHuixia J Wang

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Citations

Sep 25, 2017·Statistical Methods in Medical Research·Hanze ZhangBarbara Langland-Orban
Nov 20, 2019·Statistics in Medicine·Alessio Farcomeni, Marco Geraci
Aug 22, 2018·Journal of Dairy Science·Anke Schorr, Markus Lips
Sep 1, 2015·The Annals of Applied Statistics·Luke B SmithBrian J Reich

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