Results 221 to 230 of about 301,037 (257)
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Bayesian cusp regression and linear mixed model
2023First of all, we introduce the Bayesian mixture way of solving the Cusp Catastrophe model, which is designed to deal with piece-wise continuous outcomes. Simulation and real data analysis show that the new method beats the old stochastic different equation solution of Cusp Catastrophe model.
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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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Composite quantile regression for high-dimensional linear mixed effects models
Journal of Statistical Computation and SimulationYiping Yang +3 more
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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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The Consistency of Quantile Regression in Linear Mixed Models
2019Quantiles 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, 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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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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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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A method for fitting regression splines with varying polynomial order in the linear mixed model
Statistics in Medicine, 2005The 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 +3 more
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