Results 21 to 30 of about 1,975,816 (285)

Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data [PDF]

, 2015
We propose an estimation approach to analyse correlated functional data which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model.
Cederbaum, Jona, Greven, Sonja, Hoole, Phil, Pouplier, Marianne   +3 more
core   +2 more sources

lmerTest Package: Tests in Linear Mixed Effects Models

Journal of Statistical Software, 2017
One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer?
Alexandra Kuznetsova, Per B. Brockhoff, Rune H. B. Christensen   +2 more
doaj   +1 more source

Federated generalized linear mixed models for collaborative genome-wide association studies

iScience, 2023
Summary: Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges.
Wentao Li, Han Chen, Xiaoqian Jiang, Arif Harmanci   +3 more
doaj   +1 more source

Accessible analysis of longitudinal data with linear mixed effects models

Disease Models & Mechanisms, 2022
Longitudinal studies are commonly used to examine possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design,
Jessica I. Murphy, Nicholas E. Weaver, Audrey E. Hendricks   +2 more
doaj   +1 more source

Violation of the Sphericity Assumption and Its Effect on Type-I Error Rates in Repeated Measures ANOVA and Multi-Level Linear Models (MLM)

Frontiers in Psychology, 2017
We investigated the effects of violations of the sphericity assumption on Type I error rates for different methodical approaches of repeated measures analysis using a simulation approach.
Nicolas Haverkamp, André Beauducel
doaj   +1 more source

Simple reparameterization to improve convergence in linear mixed models

Acta Agriculturae Slovenica, 2010
Slow convergence and mixing are one of the main problems of Markov chain Monte Carlo (McMC) algorithms applied to mixed models in animal breeding. Poor convergence is to a large extent caused by high posterior correlation between variance components and ...
Gregor GORJANC, Tina FLISAR, Jose Carlos MARTÍNEZ-ÁVILA, Luis Alberto GARCÍA-CORTÉS   +3 more
doaj   +1 more source

Simultaneous Inference in General Parametric Models [PDF]

, 2008
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level ...
Bates, Bates, Bretz, Bretz, Bullinger, Bönsch, Garcia, Genz, Genz, Genz, Hochberg, Hothorn, Hothorn, Hsu, Marcus, R Development Core Team, Rousseeuw, Salib, Searle, Serfling, Shaffer, Stefanski, Todorov, Tong, Westfall, Westfall, Westfall, Yohai, Zeileis, Zeileis   +29 more
core   +3 more sources

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence [PDF]

, 2015
A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context.
Aas K, Aristidis K Nikoloulopoulos, Brenner H, Chu H, Chu H, Demidenko E, Genest C, Genest C, Genest C, Hamza TH, Hoyer A, Hua L, Hult H, Joe H, Joe H, Joe H, Joe H, Joe H, Karageorgopoulos DE, Kurowicka D, Kuss O, Leeflang MMG, Leeflang MMG, Ma X, Mavridis D, McNeil AJ, Nash JC, Nelsen RB, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Panagiotelis A, Paul M, Serfling RJ, Shen C, Stroud AH, Vuong QH, Ye Y   +41 more
core   +2 more sources

Fiducial Inference in Linear Mixed-Effects Models

Entropy
We develop a novel framework for fiducial inference in linear mixed-effects (LME) models, with the standard deviation of random effects reformulated as coefficients.
Jie Yang, Xinmin Li, Hongwei Gao, Chenchen Zou   +3 more
doaj   +1 more source

Linear Mixed Models: Part I

, 2021
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as y = Xβ + 𝜖, where y is a vector of observations, X is a matrix of known covariates, β is a vector of unknown regression coefficients, and 𝜖 is a vector of (unobservable random ...
Jiming Jiang, Thuan Nguyen
openaire   +1 more source