Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 193 for “"mixed models"”.
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A new REML (PX)EM algorithm for linear mixed models and factor analytic mixed models
Linear mixed models and factor analytic mixed models are routinely applied to biological data arising from designed experiments. The preferred method for estimating the parameters associated with these models is residual maximum likelihood (REML). Most statistical software packages available for …
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A new REML (PX)EM algorithm for linear mixed models and factor analytic mixed models
Linear mixed models and factor analytic mixed models are routinely applied to biological data arising from designed experiments. The preferred method for estimating the parameters associated with these models is residual maximum likelihood (REML). Most statistical software packages available for …
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Conjugate generalized linear mixed models with applications
… the development of conjugate generalized linear mixed models (CGLMMs), which is a computationally efficient modelling framework for longitudinal and multilevel data where the likelihood can be expressed in closed-form. We focus on the scenario where the random effects are mapped uniquely onto the …
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Linear Mixed Models With Non-Normal Distributions
Linear mixed models based on the normality assumption are widely used in health related studies. Although the normality assumption leads to simple, mathematically tractable, and powerful tests, violation of the assumption may easily invalidate the statistical inference. In this dissertation, we …
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Multivariate linear mixed models for statistical genetics
… To do so, we build on the classical linear mixed model (LMM), a widely adopted framework for genetic studies. The first contribution of this thesis is mtSet, an efficient mixed-model approach that enables genome-wide association testing between sets of genetic variants and multiple traits …
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Bayesian regularized quantile mixed models for longitudinal studies
… developing novel Bayesian regularized quantile mixed effect models to tackle these challenges. In the first project, we have proposed a Bayesian variable selection method in the mixed effect models for longitudinal lipidomics studies. To dissect important lipid-environment interactions, our …
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Modified BIC for Model Selection in Linear Mixed Models
Linear mixed effects models are widely used in applications to analyze clustered and longitudinal data. Model selection in linear mixed models is more challenging than that of linear models as the parameter vector in a linear mixed model includes both fixed effects and variance components …
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Likelihood Theory and Methods for Generalized Linear Mixed Models
Generalized linear mixed models are an essential group of models for analysing many present-day complex data sets, especially those that contain non-normal and correlated response data. Despite the large volume of research concerning this group of models, there is very little theory concerning the …
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Default Bayesian model determination for generalised liner mixed models
In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation.<br/><br/>Default prior distributions for the model parameters, that are based on a unit …
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GOODNESS OF FIT TESTS FOR GENERALIZED LINEAR MIXED MODELS
Generalized Linear mixed models (GLMMs) are widely used for regression analysis of data, continuous or discrete, that are assumed to be clustered or correlated. Assessing model fit is important for valid inference. We therefore propose a class of chi-squared goodness-of-fit tests for GLMMs. Our …
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Some New Estimator in Linear Mixed Models with Measurement error
Linear mixed models (LMMs) are an important tool for the analysis of a broad range of structures including longitudinal data, repeated measures data (including cross-over studies), growth and dose-response curve data, clustered (or nested) data, multivariate data, and correlated data. In many …
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Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics
Generalized linear mixed models are used to model clustered and longitudinal data in which the distribution of the response variable is a member of the exponential family. This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying …
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Missing Responses in Generalized Linear Mixed Models Where the Missingness is Nonignorable
… incomplete data using generalized linear mixed models (GLMMs). GLMMs are widely used in clustered and longitudinal data analyses, where random effects are used to model subject or cluster specific effects. We review algorithms for finding the ML estimators in GLMMs with nonignorable …
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Inference on Quantile Regression for Mixed Models With Applications to GeneChip Data
… is a valuable complement to the usual mixed model analysis based on Gaussian likelihood.
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A Comparison of Microarray Analyses: A Mixed Models Approach Versus the Significance Analysis of Microarrays
… been proposed. This paper considers two: (1) a mixed models approach, and (2) the Signiffcance Analysis of Microarrays.
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A variance shilf model for outlier detection and estimation in linear and linear mixed models
… of these methods to detecting outliers in linear mixed models has not been entirely successful, in the literature. This thesis focuses on a variance shift outlier model as an approach to detecting and assessing outliers in both linear fixed effects and linear mixed effects analysis. A variance …
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A variance shift model for outlier detection and estimation in linear and linear mixed models
… of these methods to detecting outliers in linear mixed models has not been entirely successful, in the literature. This thesis focuses on a variance shift outlier model as an approach to detecting and assessing outliers in both linear fixed effects and linear mixed effects analysis. A variance …
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Variable selection for generalized linear mixed models and non-Gaussian Genome-wide associated study data
Genome-wide associated study (GWAS) aims to identify associated single nucleotide polymorphisms (SNP) for phenotypes. SNP has the characteristic that the number of SNPs is from hundred of thousands to millions. If p is the number of SNPs and n is the sample size, it is a p>>n variable selection …
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Optimal Design of Single Factor cDNA Microarray experiments and Mixed Models for Gene Expression Data
… on a large scale. E- and A-optimality of mixed designs was established for experiments with up to 26 different varieties and with the restriction that the number of arrays available is equal to the number of varieties. Because the IBD setting only allows for a single blocking factor …
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