Results 11 to 20 of about 1,319,586 (359)
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
semanticscholar +4 more sources
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, Qian Chen, Alfred Soboyejo
semanticscholar +4 more sources
Extension of theglmm.hppackage to zero-inflated generalized linear mixed models and multiple regression [PDF]
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
openalex +2 more sources
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
doaj +2 more sources
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
doaj +3 more sources
Linear Mixed Model Robust Regression [PDF]
, 2001 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
Megan J. Waterman +2 more
openalex +1 more source
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].
Beate Weidenhammer
openalex +3 more sources
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 ...
Müller, Samuel +2 more
core +2 more sources
JULIAN J. FARAWAY. Extending the Linear Model with R: Generalized Linear, Mixed Effects, and Nonparametric Regression Models, 2nd edition. Boca Raton: CRC Press [PDF]
, 2017 Andrzej T. Gałecki
openalex +2 more sources
Gradient boosting for linear mixed models [PDF]
The International Journal of Biostatistics, 2020 Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory.
C. Griesbach +2 more
semanticscholar +1 more source