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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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Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines [PDF]
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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Estimating functional linear mixed-effects regression models [PDF]
Computational Statistics & Data Analysis, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Baisen +2 more
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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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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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Model Selection in Linear Mixed Models [PDF]
, 2013 Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other ...
Samuel Müller, J. Scealy, A. Welsh
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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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iForest - Biogeosciences and Forestry, 2016 The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area.
López-Serrano Pablito M +6 more
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Non-linear and mixed regression models in predicting sustainable concrete strength
Construction and Building Materials, 2018 Abstract Most previous research adopting the regression analysis to capture the relationship between concrete properties and mixture-design-related variables was based on the linear approach with limited accuracy. This study applies non-linear and mixed regression analyses to model properties of environmentally friendly concrete based on a ...
Ruoyu Jin +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 ...
Jiangshan Lai +3 more
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