Quantile regression in linear mixed models: a stochastic approximation EM approach

DOI10.4310/SII.2017.v10.n3.a10zbMath1388.62201OpenAlexW2584465715WikidataQ45071977 ScholiaQ45071977MaRDI QIDQ1748666

Christian E. Galarza, Dipankar Bandyopadhyay, Victor Hugo Lachos

Publication date: 14 May 2018

Published in: Statistics and Its Interface (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4310/sii.2017.v10.n3.a10

zbMATH Keywords

likelihoodSAEM algorithmasymmetric Laplace distribution

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) General nonlinear regression (62J02) Stochastic approximation (62L20)

Related Items (6)

Likelihood-based inference for censored linear regression models with scale mixtures of skew-normal distributions ⋮ Shrinkage estimation of fixed and random effects in linear quantile mixed models ⋮ A mixed stochastic approximation EM (MSAEM) algorithm for the estimation of the four-parameter normal ogive model ⋮ Heavy-tailed longitudinal regression models for censored data: a robust parametric approach ⋮ Constrained Bayesian doubly elastic net Lasso for linear quantile mixed models ⋮ qrLMM

Uses Software

• R

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