Results 61 to 70 of about 776,217 (345)
bioRxiv, 2021 In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs).
Ariel I. Mundo +2 more
semanticscholar +1 more source
mplot: An R Package for Graphical Model Stability and Variable Selection Procedures
Journal of Statistical Software, 2018 The mplot package provides an easy to use implementation of model stability and variable inclusion plots (Müller and Welsh 2010; Murray, Heritier, and Müller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008; Jiang, Nguyen, and Rao ...
Garth Tarr +2 more
doaj +1 more source
Generalized semiparametrically structured mixed models [PDF]
, 2001 Generalized linear mixed models are a common tool in statistics which extends generalized linear models to situations where data are hierarchically clustered or correlated.
Tutz, Gerhard
core +1 more source
Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms [PDF]
, 2012 The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models.
Browne, William J. +2 more
core +2 more sources
Analysis of neonatal clinical trials with twin births
BMC Medical Research Methodology, 2009 Background In neonatal trials of pre-term or low-birth-weight infants, twins may represent 10–20% of the study sample. Mixed-effects models and generalized estimating equations are common approaches for handling correlated continuous or binary data ...
Shaffer Michele L +2 more
doaj +1 more source
A stochastic variational framework for fitting and diagnosing generalized linear mixed models
, 2014 In stochastic variational inference, the variational Bayes objective function is optimized using stochastic gradient approximation, where gradients computed on small random subsets of data are used to approximate the true gradient over the whole data set.
Nott, David J., Tan, Linda S. L.
core +1 more source
Genetics Selection Evolution, 2005 We propose a general Bayesian approach to heteroskedastic error modeling for generalized linear mixed models (GLMM) in which linked functions of conditional means and residual variances are specified as separate linear combinations of fixed and random ...
Kizilkaya Kadir, Tempelman Robert J
doaj +1 more source
A Note on the Identifiability of Generalized Linear Mixed Models [PDF]
, 2014 I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable.
Labouriau, Rodrigo
core
Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data [PDF]
, 2003 Non-Gaussian point-referenced spatial data are frequently modeled using generalized linear mixed models (GLMM) with location-specific random effects. Spatial dependence can be introduced in the covariance matrix of the random effects.
Gemperli, Armin, Vounatsou, Penelope
core +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Despite their increased risk for functional impairment resulting from cancer and its treatments, few adolescents and young adults (AYAs) with a hematological malignancy receive the recommended or therapeutic dose of exercise per week during inpatient hospitalizations.
Jennifer A. Kelleher +8 more
wiley +1 more source