Results 261 to 270 of about 12,232,356 (294)

Rényi statistics for testing hypotheses in mixed linear regression models

Journal of Statistical Planning and Inference, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Molina, I., Morales, D.
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Composite quantile regression for high-dimensional linear mixed effects models

Journal of Statistical Computation and Simulation
Gaorong Li, Xiaogang Duan
exaly   +2 more sources

Bayesian quantile regression for skew-normal linear mixed models

Communications in Statistics - Theory and Methods, 2016
AbstractLinear 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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Stepwise Regression in Mixed Quantitative Linear Models with Autocorrelated Errors

Communications in Statistics - Simulation and Computation, 2006
ABSTRACT 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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The Consistency of Quantile Regression in Linear Mixed Models

2019
Quantiles are parameters of a distribution, which are of location and of scale character at the same time. The median, as a location parameter, is even robust and outperforms the mean, whenever there are outliers or extreme values in the data. In linear models quantile regression was firstly introduced by Koenker and Bassett [1978].
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Several nonparametric and semiparametric approaches to linear mixed model regression

Journal of Statistical Computation and Simulation, 2013
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 (P) models, correctness of the assumed model is critical for the validity of the ensuing inference.
Megan J. Waterman, Jeffrey B. Birch, Abdel-Salam G. Abdel-Salam   +2 more
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Principal components regression and r-k class predictions in linear mixed models

Linear Algebra and its Applications, 2018
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Özkale M.R., Kuran Ö.
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A method for fitting regression splines with varying polynomial order in the linear mixed model

Statistics in Medicine, 2005
The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial
Lloyd J, Edwards, Paul W, Stewart, James E, MacDougall, Ronald W, Helms   +3 more
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A Revisit to the Application of Weighted Mixed Regression Estimation in Linear Regression Models with Missing Data

2001
This paper deals with the application of the weighted mixed regression estimation of the coefficients in a linear model when some values of some of the regressors are missing. Taking the weight factor as an arbitrary scalar, the performance of weighted mixed regression estimator in relation to the conventional least squares and mixed regression ...
Toutenburg, Helge, Srivastava, V. K.
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Linear Mixed Model Robust Regression

2014
Ph. D.
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