Results 41 to 50 of about 570,704 (178)

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., Carpenter, James R., Goldstein, Harvey   +2 more
core   +2 more sources

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

A general approach to mixed effects modeling of residual variances in generalized linear mixed models

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

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

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, Kunselman Allen R, Watterberg Kristi L   +2 more
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  

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

Sensory analysis of Prato cheeses by generalized linear mixed models

Revista do Instituto de Latícinios Cândido Tostes
Sensory analysis, an area of Food Science, is used to analyze and measure characteristics of foods, being able to evaluate the acceptance of samples. Such assessments can be performed using the 9-point numerical hedonic scale, classified as an ordinal ...
Tatiane Carvalho Alvarenga, Jéssica Ferreira Rodrigues   +1 more
doaj   +1 more source

Basic Features of the Analysis of Germination Data with Generalized Linear Mixed Models

Data, 2020
Germination data are discrete and binomial. Although analysis of variance (ANOVA) has long been used for the statistical analysis of these data, generalized linear mixed models (GzLMMs) provide a more consistent theoretical framework. GzLMMs are suitable
Alberto Gianinetti
doaj   +1 more source

Variational Inference for Generalized Linear Mixed Models Using Partially Noncentered Parametrizations

, 2013
The effects of different parametrizations on the convergence of Bayesian computational algorithms for hierarchical models are well explored. Techniques such as centering, noncentering and partial noncentering can be used to accelerate convergence in MCMC
Nott, David J., Tan, Linda S. L.
core   +1 more source