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Bayesian composite quantile regression for linear mixed-effects models
Communications in Statistics - Theory and Methods, 2017ABSTRACTLongitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors.
Yuzhu Tian, Heng Lian, Maozai Tian
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A bayesian mixture model with linear regression mixing proportions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is to learn how complex data distributions evolve. We develop models and Bayesian learning algorithms for inferring the temporal trends of the components in a mixture model as a
Xiuyao Song +3 more
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Linear Mixed Model Robust Regression [PDF]
, 2019 Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. An incorrectly specified parametric means model may be
Waterman, Megan J. +2 more
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Bayesian quantile regression for skew-normal linear mixed models
Communications in Statistics - Theory and Methods, 2016AbstractLinear mixed models have been widely used to analyze repeated measures data which arise in many studies. In most applications, it is assumed that both the random effects and the within-subjects errors are normally distributed. This can be extremely restrictive, obscuring important features of within-and among-subject variations.
A. Aghamohammadi, M. R. Meshkani
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Rényi statistics for testing hypotheses in mixed linear regression models
Journal of Statistical Planning and Inference, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Molina, I., Morales, D.
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A new stochastic mixed ridge estimator in linear regression model
Statistical Papers, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Yalian, Yang, Hu
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Generalized linear mixed model for monitoring autocorrelated logistic regression profiles
The International Journal of Advanced Manufacturing Technology, 2012Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems.
Mehdi Koosha, Amirhossein Amiri
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Stepwise Regression in Mixed Quantitative Linear Models with Autocorrelated Errors
Communications in Statistics - Simulation and Computation, 2006ABSTRACT In the stepwise procedure of selection of a fixed or a random explanatory variable in a mixed quantitative linear model with errors following a Gaussian stationary autocorrelated process, we have studied the efficiency of five estimators relative to Generalized Least Squares (GLS): Ordinary Least Squares (OLS), Maximum Likelihood (ML ...
Gülhan Alpargu, Pierre Dutilleul
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Several nonparametric and semiparametric approaches to linear mixed model regression
Journal of Statistical Computation and Simulation, 2013Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric (P) models, correctness of the assumed model is critical for the validity of the ensuing inference.
Megan J. Waterman +2 more
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Principal components regression and r-k class predictions in linear mixed models
Linear Algebra and its Applications, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Özkale M.R., Kuran Ö.
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