Estimating Functional Linear Mixed-Effects Regression Models [PDF]
Computational Statistics & Data Analysis, 2016 The functional linear model is a popular tool to investigate the relationship between a scalar/functional response variable and a scalar/functional covariate. We generalize this model to a functional linear mixed-effects model when repeated measurements are available on multiple subjects.
Baisen Liu, Jiguo Cao
semanticscholar +7 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
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 +10 more
doaj +5 more sources
Bayesian cusp regression and linear mixed model [PDF]
, 2022 First 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.
Jiayi Hou
openalex +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 ...
Bernadette Govaerts +4 more
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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 [PDF]
BMC Medical Research MethodologyIntroduction 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 +4 more
doaj +4 more sources
Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models [PDF]
2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP), 2021 Mixed linear regression (MLR) model is among the most exemplary statistical tools for modeling non-linear distributions using a mixture of linear models. When the additive noise in MLR model is Gaussian, Expectation-Maximization (EM) algorithm is a widely-used algorithm for maximum likelihood estimation of MLR parameters.
Babak Barazandeh +3 more
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Critical evaluation of linear regression models for cell-subtype specific methylation signal from mixed blood cell DNA. [PDF]
PLoS ONE, 2018 Epigenome-wide association studies seek to identify DNA methylation sites associated with clinical outcomes. Difference in observed methylation between specific cell-subtypes is often of interest; however, available samples often comprise a mixture of ...
Daniel W Kennedy +8 more
doaj +2 more sources
Forest Systems, 2016 Aim of the study: The main objective of this study was to test Geographically Weighted Regression (GWR) for developing height-diameter curves for forests on a large scale and to compare it with Linear Mixed Models (LMM).
María Quirós Segovia +2 more
doaj +3 more sources