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.
Results
Showing 1 to 20 of 120 for “"Linear Mixed Models"”.
-
Conjugate generalized linear mixed models with applications
… on 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 …
-
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 …
-
Multivariate linear mixed models for statistical genetics
… traits. 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 …
-
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 …
-
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 …
-
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 …
-
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 …
-
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 …
-
Missing Responses in Generalized Linear Mixed Models Where the Missingness is Nonignorable
… for analyzing 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 …
-
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 …
-
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 …
-
A variance shilf model for outlier detection and estimation in linear and linear mixed models
… are established methods in detecting outliers in linear fixed effects analysis. The extension 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 …
-
A variance shift model for outlier detection and estimation in linear and linear mixed models
… are established methods in detecting outliers in linear fixed effects analysis. The extension 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 …
-
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 …
-
Bayesian variable selection for linear mixed models when p is much larger than n with applications in genome wide association studies
… can be seen as a variable selection problem in linear mixed models (LMMs) where $p$ is much larger than $n$. To deal with the $p>>n$ issue, our three proposed methods use novel Bayesian approaches based on two steps: a screening step and a model selection step. To control false discoveries, we …
-
Variational Approximation for Complex Regression Models
… has resulted in the need for more flexible models and fast computational approximations. My thesis reflects these themes by considering some very flexible regression models and developing fast variational approximation methods for fitting them under a Bayesian framework. Models considered …
-
Mixture Modeling for Multivariate Observations
… to extend the application of these mixture models to estimate nonparametrically a multivariate distribution. The major difficulty with the likelihood approach that is associated with the estimation of these mixtures in the multivariate case is that the likelihood function cannot be maximized …
-
A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction
Linear mixed models have long been the method of choice for risk prediction analysis on high-dimensional data, where random effect terms are used to capture predictive effects from multiple markers. However, it remains computationally challenging to simultaneously model a large number of variables …
-
Patterns of helminth and protozoan parasite infections in bighorn sheep, Ovis canadensis: sex, season and activity
… spending more time grazing. Generalised linear mixed models suggest that parasite FECs are different for male and female bighorn sheep between winter and non-winter seasons. However, the pattern of FECs between the sexes differ based on the parasite group. Strongyle FEC was significantly …
Page 1 of 6