Mixed Models
Generalized linear mixed models for count data
... In Chapter 3, we will discuss the methods of modeling the overdispersion [16] in Poisson mixed models (the GLMMs there the conditional distribution of response variables condition on the random effects is ...
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Credibililty Theory for Generalized Linear and Mixed Models
... many observations are needed in a risk class of a segmented portfolio before the GLM estimator can be considered credible? Schmitter (2004) provides an excellent simple method to estimate the number of claims that will ...
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Adaptability, Stability and Multivariate Selection by Mixed Models
... Besides the best genotypes recommendation through the interaction free ge- notypic values ( u + g ), a general recommendation for all environments of the experimental net can be realized by the capitalization of the mean ...
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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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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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Limited Resequencing for Mixed Models with Multiple Objectives
... This research presents a problem relevant to production scheduling for mixed models—production schedules that contain several unique items, but each unique item may have multiple units that require ...
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Analysis of generalised mixed models for categorical data
... regression models to study the inter relationships among variables is a common phenomenon in socio-economic and biomedical research and the related theory has occupied a substantial part of the statistical ...the ...
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Estimation for Multivariate Linear Mixed Models
... linear mixed models and then continue to multivariate linear models without ...linear mixed models will be included in next section. Linear mixed models are statistical ...
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Latent mixed models
... linear mixed model has been a major research interest of Dr Arthur Gilmour, motivated by problems arising in research data generated by agricultural ...linear mixed models and exploiting sparsity to ...
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Inference for generalised linear mixed models with sparse structure
... in models in which there is no obvious simplification to the high-dimensional integral involved in the ...sparse models, in which little information is available on each random ...in models with m = ...
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Bayesian Generalized Kernel Mixed Models
... We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a reproducing ...
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Streamlined variance calculations for semiparametric mixed models
... Models (1) and (2) featured random intercepts: the difference between the fitted subject response and estimated population mean curve is constant. This may not in general be realistic; the subject-specific ...
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Bayesian Inference for Spatial Beta Generalized Linear Mixed Models
... study. Mixed beta regression models for both the mean and precision parameters were proposed by ...MCMC mixed beta regression models were elaborated by ...
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Analysis of Gene Expression Profiles with Linear Mixed Models
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Semiparametric Mixed Models for Censored Longitudinal Data.
... In this chapter, we have demonstrated the usage of smoothing splines in analyzing censored longitudinal data within the semiparametric mixed model framework (3.1). By extending the maximum penalized likelihood ...
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Some problems in estimation in mixed linear models
... a mixed model was in the work of astronomers Airy (1861) and Chauvenet (1863), who used two-component balanced ...of mixed models consisted of equating various sums of squares of the data to their ...
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Performance of mixed effects models in the analysis of mediated longitudinal data
... the mixed model provides similar power and coverage prob- ability to the ...additional mixed models (e.g. models with and without mediating factors), it is a less efficient approach compared ...
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An efficient computing strategy for prediction in mixed linear models
... Lane and Nelder (1982) describe a general approach for forming predictions in gen- eral(ised) linear models. BrieAy, their approach involves forming the 5tted values for all combinations of the variables in the ...
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Forecasting bubbles with mixed causal noncausal autoregressive models
... MAR models to predict both explosive and stable episodes remains scarce (see also Lanne, Nyberg, and Saarinen, 2012 and Gouri´eroux, Hencic, and Jasiak, ...MAR(r,1) models, with unconstrained r number of ...
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Forecasting bubbles with mixed causal noncausal autoregressive models
... (2016), which furthermore incorporates all past realised values of the series. The difference between them, reported in the last column, represents how much of the sample-based probabilities was induced by the learning ...
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