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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 ...
R. Jin, Qian Chen, A. Soboyejo
semanticscholar   +6 more sources

Estimating functional linear mixed-effects regression models [PDF]

Computational Statistics & Data Analysis, 2016
A new functional linear mixed model is proposed to investigate the impact of functional predictors on a scalar response when repeated measurements are available on multiple subjects.
Baisen Liu, Liangliang Wang, Jiguo Cao
semanticscholar   +7 more sources

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
doaj   +5 more sources

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, Andrada Ivanescu, Mayuko Saito, Ciprian Crainiceanu, Devan Jaganath, Robert H. Gilman, Jean E. Crabtree, Dermott Kelleher, Lilia Cabrera, Vitaliano Cama, William Checkley   +10 more
doaj   +5 more sources

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
doaj   +4 more sources

Extension of the glmm.hp package 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, Weijie Zhu, Dongfang Cui, Lingfeng Mao   +3 more
semanticscholar   +2 more sources

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, V. H. Lachos, D. Bandyopadhyay   +2 more
semanticscholar   +3 more sources

The Essentials on Linear Regression, ANOVA, General Linear and Linear Mixed Models for the Chemist [PDF]

, 2020
This text provides a brief and accessible guide for implementing general, ANOVA and linear mixed models for the analysis of real world data from chemistry, industrial, bio or life-sciences experiments. The reader is introduced to the main concepts and vocabulary of the subject and to the main statistical methods available for formalizing, estimating ...
Govaerts, Bernadette, Francq, Bernard G., Marion, Rebecca, Martin, Manon, Thiel, Michel   +4 more
openaire   +5 more sources

Insight into Genome-Wide Associations of Growth Trajectories Using a Hierarchical Non-Linear Mixed Model [PDF]

Biology
In 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, Li’ang Yang, Weiguo Cui, Runqing Yang   +3 more
doaj   +2 more sources

Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson’s disease over seven years in a cohort: a comparative analysis

BMC Medical Research Methodology
Introduction While there is an interest in defining longitudinal change in people with chronic illness like Parkinson’s disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers.
Anne-Marie Hanff, Rejko Krüger, Christopher McCrum, Christophe Ley, NCER-PD   +4 more
doaj   +3 more sources