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 428 for “"Covariate"”.
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High-Dimensional Covariate-Dependent Gaussian Graphical Models
In this dissertation, we propose a covariate-dependent Gaussian graphical model (cdexGGM) for capturing network structure that varies with covariates through a novel parameterization. Utilizing a likelihood framework, our methodology jointly estimates all edge and vertex parameters. We further …
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Semiparametric Regression Methods with Covariate Measurement Error
… inference about the relationships between the covariates and the response. We investigate measurement error in the covariates of two types of regression models. For each we propose a fully Bayesian approach that treats the variable measured with error as a latent variable to be integrated over, …
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Toward Nonparametric Propensity Score Estimation With Guaranteed Covariate Balance
… studies, which leads to imbalance in covariate distributions </em><em>between treatments and biased treatment effect inference. The propensity score </em><em>(PS) has been widely used to adjust this covariate imbalance in observational data. However, </em><em>the propensity score …
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Cure Rate Models with Nonparametric Form of Covariate Effects
… have developed a nonparametric model where the covariate effects on both the cure probability and the hazard component are estimated by smoothing splines. Our first development falls under the same framework but estimates the hazard component based on the accelerated failure time model, instead …
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Covariate-adjusted ROC regressions and the extensions in trend tests.
… model (GLM) framework enabled one to investigate covariate effects to model the ROC using parametric and semi-parametric regression methods. The latest addition to this ongoing investigation into problems of this type made use of the beta regression for placement values and the property that their …
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On the Use of Grouped Covariate Regression in Oversaturated Models
… techniques improve, oftentimes the number of covariates exceeds the number of observations. When this happens, regression models become oversaturated and, thus, inestimable. Many classical and Bayesian techniques have been designed to combat this difficulty, with various means of combating the …
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Spatial modeling, covariate measurement error and design issues in environmental epidemiology
… lack of modeling strategies for individual level covariate data is a limitation of the existing methodology. We propose an individual level covariate adjusted conditional auto-regressive (indiCAR) model to incorporate both individual and area level covariates while adjusting for spatial …
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Count regression models with a misclassified binary covariate : a Bayesian approach.
… associated with the binary exposure covariate. In the first portion of the analysis, diffuse priors are utilized for the regression coefficients and the effective prior sample size technique is implemented to construct informative priors for the misclassification parameters. In the …
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Robust Efficient Estimation of Semiparametric Covariate Models based on Minimum Hellinger Distance
Covariate models, such as polynomial regression models, generalized linear models, and heteroscedastic models, are widely used in statistical applications. The importance of such models in statistical analysis is abundantly clear by the ever-increasing rate at which articles on covariate models are …
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A covariate-adjusted classification model for multiple biomarkers in disease screening and diagnosis
… and diagnosis. However, it is seldom to include covariates such as gender and age at diagnosis into these classification procedures. It is known that biomarkers or patient outcomes are often associated with some covariates in practice, therefore the inclusion of covariates may further improve the …
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Topics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination.
… Weibull proportional hazards model with ordered covariate misclassification and provide an adjustment for it. Finally, we consider informative hypotheses (Hoijtink, 2012) and perform sample size determination for this problem using the two priors approach of Brutti et al. (2008).
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Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate
We provide a Frequentist-Bayesian hybrid test statistic in this dissertation for two testing problems. The first one is to design a test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X …
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A factor model to forecast Colombian inflation using a One Covariate at a Time - Multiple Testing approach (OCMT)
Este artículo estudia la construcción de un modelo de inflación y su pronóstico utilizando un amplio conjunto de predictores. La decisión sobre el número óptimo de factores comunes se toma utilizando los criterios de información de Bai y Ng (2002); y se implementa un enfoque de una covariable a la …
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Monte Carlo simulation with parametric and nonparametric analysis of covariance for nonequivalent control groups
… of covariance and parametric ANCOVAS with a covariate correction are some of the more frequently completed analyses used in nonequivalent control group research, comparative studies with nonparametric counterparts should be completed and results compared with those more commonly used forms of …
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Addressing two issues in machine learning : interpretability and dataset shift
… a shortcoming of a popular approach to handling covariate shift, in which the training distribution and that for which predictions need to be made have different covariate distributions. In particular, the existing importance weighting approach to handling covariate shift suffers from high …
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Joint Models for Longitudinal Analysis and Competing Risks in Survival Analysis
… event(s) may lead to bias in the estimation of covariate effects. To address the problems created by these dependencies, we propose and apply several models: 1. Linking the Cause-specific Hazards Model for competing risks analyses with a model for longitudinal ordinal measurements through a …
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Variable Selection and Decision Trees: The DiVaS and ALoVaS Methods
… in decision trees. Our approach facilitates covariate selection explicitly in the model, something not present in previous research. We define a transformation that allows us to use priors from linear models to facilitate covariate selection in decision trees. Using this transform, we modify …
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Exploring cis-regulatory models of the genome to predict epigenetic state and variation
… by introducing arbitrary base-pair resolution covariates. We show that the addition of base-pair resolution chromatin accessibility covariate greatly aids in the prediction of cis-regulatory marks. Additionally, we show that by using cell-type specific covariates, CCM+ can generalize across …
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Corporate Default Predictions and Methods for Uncertainty Quantifications
… risks based on large-scale time-to-event and covariate data in the context of controlling credit risks. Specifically, we propose a competing risks model to incorporate exits of companies due to default and other reasons. Because of the stochastic and dynamic nature of the corporate risks, we …
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