Linear Mixed Models
Estimation for Multivariate Linear Mixed Models
... Linear mixed models or variance components models have been effectively and extensively used by statisticians for analyzing data when the response is ...for mixed ordinal or discrete ...
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CAS: Estimation and Robustness of Linear Mixed Models in Credibility Context
... In this paper, linear mixed models are employed for estima- tion of structural parameters in credibility context. In par- ticular, Hachemeister’s model and Dannenburg’s crossed classification model ...
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Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study
... In generalized linear mixed models, the parame- ters are estimated by maximum likelihood. However, because of nonlinearity of the model and the pres- ence of random effects, the marginal distribution ...
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Inference for generalised linear mixed models with sparse structure
... generalised linear mixed models is ...some models with sparse structure, for which none of the currently available methods appears to work ...
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Evaluation of Third Order Method for the Tests of Variance Component in Linear Mixed Models
... In this section, we presented simulation results for the tests of variance component in linear mixed models. The simulation results demonstrated that the third-order method is accurate and ...
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Hierarchical selection of fixed and random effects in generalized linear mixed models
... Table 1: Simulation results for linear mixed models. Performance was assessed the mean number false positives (FP) and false negatives (FN) for the fixed effects, the percentage of datasets with ...
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Flexible linear mixed models with improper priors for longitudinal and survival data
... hierarchical linear mixed models which are used frequently for both longitudinal and survival modelling in a number of application areas, such as medicine, epidemiology and ...These models are ...
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Generalized linear mixed models for count data
... traditional linear statistical models (LMs) have been developed primarily for normally distributed ...generalized linear models (GLMs [28]) extend LMs to include a broader class of of ...
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Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection
... and one or more explanatory variables. They provide a general method to anal- yse quantified relationships between variables within a data set in an easily inter- pretable way. A standard assumption is that the outcomes ...
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A sequential reduction method for inference in generalized linear mixed models
... generalized linear mixed models rely on approximations to the likelihood which may be of poor quality if there is little information available on each random ...
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Small Area Estimation Under Linear and Generalized Linear Mixed Models With Time and Area Effects
... For the normal error model, the interrelationship between BLUP (MPQL) with maximum likelihood (ML) and restricted or residual maximum likelihood (REML) estimators was developed in Harville (1977) and investigated further ...
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Power analysis for generalized linear mixed models in ecology and evolution
... complex models commonly used in EE ...generalized linear mixed models (GLMMs) as a flexible and accessible approach to power analysis that can account for random effects, overdispersion and ...
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Local influence diagnostics for generalized linear mixed models with overdispersion
... Local influence was studied before as a means to detect outlying subjects, and features thereof, for the linear mixed model and some generalized linear mixed models. We have extended ...
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Clustering in linear mixed models with a group fused lasso penalty
... normal linear mixed models the AIC is not as straightforward as in normal linear models (compare Vaida and Blanchard (2005) and Greven and Kneib ...in linear mixed ...
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Gaussian Variational Approximate Inference for Generalized Linear Mixed Models
... Variational approximation methods have become a mainstay of contemporary Machine Learn- ing methodology, but currently have little presence in Statistics. We devise an effective vari- ational approximation strategy for ...
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Multiple testing correction in linear mixed models
... Multiple hypothesis testing is an essential step in GWAS analysis. Although the correct per-marker threshold dif- fers as a function of species, marker densities, genetic relatedness, and trait heritability, no previous ...
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Analysis of multivariate longitudinal kidney function outcomes using generalized linear mixed models
... Generalized Linear Mixed Models (mGLMM) to analyze multiple longitudinal kidney function outcomes collected over 3 years on a cohort of 110 renal transplanta- tion ...
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Identifying Trends and Patterns in Incidence of AIDS in Bangkok Using Generalised Linear Mixed Models
... generalized linear mixed models ...statistical models that can cope with a wide range of response distri- butions, including binomial, Poisson and negative binomial and, like the marginal ...
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Analysis of baseline, average, and longitudinally measured blood pressure data using linear mixed models
... Hypertension is a major morbidity and mortality risk fac- tor for stroke, myocardial infarction, heart failure, and end-stage renal disease [1]. It is a multifactorial disorder resulting from inheritance of several ...
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Random Effect Selection in Linear Mixed Models.
... The selection of random effects in linear mixed models is an important yet chal- lenging problem in practice. We propose a robust and unified framework for au- tomatically selecting random effects and ...
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