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Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression [PDF]
Journal of Statistical Software, 2014 Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a ?exible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or
Marco Geraci
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Estimating functional linear mixed-effects regression models [PDF]
Computational Statistics and Data Analysis, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Baisen Liu +2 more
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Journal of Plant Ecology, 2023 glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has rapidly gained recognition and popularity among ecologists ...
Lingfeng Mao, Mao Lingfeng
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Study of Bayesian variable selection method on mixed linear regression models.
PLoS ONE, 2023 Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model ...
Yong Li, Hefei Liu, Rubing Li
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Emerging Themes in Epidemiology, 2016 Background Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes.
Laura M. Grajeda +10 more
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Quantile regression in linear mixed models: a stochastic approximation EM approach [PDF]
Statistics and Its Interface, 2017 This paper develops a likelihood-based approach to analyze quantile regression (QR) models for continuous longitudinal data via the asymmetric Laplace distribution (ALD). Compared to the conventional mean regression approach, QR can characterize the entire conditional distribution of the outcome variable and is more robust to the presence of outliers ...
Christian E, Galarza +2 more
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Insight into Genome-Wide Associations of Growth Trajectories Using a Hierarchical Non-Linear Mixed Model [PDF]
BiologyIn applying a hierarchical mixed model to genome-wide association analysis (GWAS) of longitudinal data, dimensionality reduction through modeling repeated measurements improves both computational efficiency and statistical power. Legendre polynomials can
Ying Zhang +3 more
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Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models [PDF]
2019 IEEE 58th Conference on Decision and Control (CDC), 2019 We consider {\em Mixed Linear Regression (MLR)}, where training data have been generated from a mixture of distinct linear models (or clusters) and we seek to identify the corresponding coefficient vectors. We introduce a {\em Mixed Integer Programming (MIP)} formulation for MLR subject to regularization constraints on the coefficient vectors.
Taiyao Wang, Ioannis Paschalidis
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Polygenic modeling with bayesian sparse linear mixed models. [PDF]
PLoS Genetics, 2013 Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies.
Xiang Zhou +2 more
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Forests, 2017 Crown profile is mostly related to the competition of individual trees in the stands, light interception, growth, and yield of trees. A total of 76 sample trees with a total number of 889 whorls and 3658 live branches were used to develop the outer crown
Fengri Li, Huilin Gao, Li Fengri
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