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 19 of 19 for “"Missing at Random"”.
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Robustness of Multiple Imputation under Missing at Random (MAR) Mechanism: A Simulation Study
<p>Missing data is an unavoidable issue in controlled clinical trials and public health research and practice. Presence of missing data and applying inappropriate methods of analysis generates biased estimates and reduces power of study. It is very important for investigators to use appropriate …
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Sensitivity analysis approaches for incomplete longitudinal data in a multi-centre clinical trial
… is the development of sensitivity analysis strategy for dealing with incomplete longitudinal data. The second important contribution is setting up of simulation experiment to evaluate the performance of some of the sensitivity analysis approaches. The third contribution is that the thesis …
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Using phenotyped but ungenotyped relatives in genetic association tests
… there are individuals for whom rich phenotypic data have been collected, but who died before providing DNA for genetic studies. Genotypes of their relatives are often available. The main question we address is how and when one should incorporate phenotyped but ungenotyped relatives into genetic …
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Selected topics in statistical discriminant analysis.
This dissertation consists of three selected topics in statistical discriminant analysis: dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction method to determine a low-dimensional hyperplane that preserves or nearly …
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Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling
… supervised machine learning problem for well rate estimations utilizing well test features that are far from independent and identically distributed (IID), and exhibit missing data with a not missing at random (MNAR) classification from three different oil fields. This research introduces a …
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THREE ESSAYS ON THE INFLUENCE OF PEERS AND PRIMARY CARE ENGAGEMENT
In this dissertation, I study econometric issues in network and health economics. Measurement error is a ubiquitous problem in the peer effects literature that is not well understood. In Chapter 1, ``Measurement error in peer effects,'' I develop a constructive approach to empirically assess the …
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Unified Approach to Partially Linear Model and Cox Proportional Hazards Model with Missing Covariates
In regression analysis the problem of missing covariate data is common in various fields of application. Many methods have been developed to deal with this problem in the past three decades. These methods are workable under most missing data scenarios. However, when missing covariate data appear in …
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Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data
Ecological data is often fraught with many problems such as Missing Data and Spatial Correlation. In this dissertation we use a data set collected by the Ohio EPA as motivation for studying techniques to address these problems. The data set is concerned with the benthic health of Ohio's waterways. …
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USING PRINCIPAL COMPONENT ANALYSIS (PCA) TO OBTAIN AUXILIARY VARIABLES FOR MISSING DATA IN LARGE DATA SETS
The purpose of this dissertation is to address an important issue in the imputation of missing data in large data sets. The issue can arise in any analysis in which auxiliary variables are used to inform a modern missing data handling procedure (e.g., FIML, MI) to support the missing at random …
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Missing data and multiple imputation: A sampling study with the SAGA cohort
Background: Missing data in epidemiological research is a common occurrence where there is no method that conclusively performs best. The aim of this thesis is to compare selected methods on the Stress-And Gene-Analysis (SAGA) cohort. Methods: Using: Complete case analysis (CCA), Single imputation …
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A Comparison for Longitudinal Data Missing Due to Truncation
Many longitudinal clinical studies suffer from patient dropout. Often the dropout is nonignorable and the missing mechanism needs to be incorporated in the analysis. The methods handling missing data make various assumptions about the missing mechanism, and their utility in practice depends on …
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Sufficient Dimension Reduction with Missing Data
… (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) approach proposed by Li (1991). We show …
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Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
… therefore, assumes monotone dose-response relationship. Also, the logistic regression model requires the response to be categorical and, thus, it is not applicable for continuous data. The traditional design in Phase II determines if a new drug will be further tested in Phase III based on only …
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Methods for handling missing data in cohort studies where outcomes are truncated by death
This dissertation addresses problems found in observational cohort studies where the repeated outcomes of interest are truncated by both death and by dropout. In particular, we consider methods that make inference for the population of survivors at each time point, otherwise known as 'partly …
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Three Papers on the Black-White Mobility Gap in the United States
Paper 1: Missing at Random? An Analysis of the Effect of Sample Selection on Intergenerational Earnings Elasticities by Race Utilizing the Panel Study of Income Dynamics, I assess the effect of sample selection bias on estimates of intergenerational earnings elasticities for white and black …
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Evaluation of Imputation Methods Focusing on Categorical Outcomes
<p>In general, standard statistical analysis models typically rely on completely observed cases, excluding incomplete rows from the dataset. This approach poses particular challenges when the objective is to predict a rare outcome, especially when some of the ob servations with the rare outcome are …
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Handling Missing Data in the Multivariate Growth Curve Model: Comparative Evaluations Using Extensive Simulations
The multivariate growth curve models (GCMs) are generalized multivariate analysis of variance (GMANOVA) models useful in analyzing of longitudinal data, growth curves or other datasets involving response curves. Unlike the MANOVA model and traditional linear models (eg. generalized linear mixed …
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Kidnappings by violent political groups: Explaining between-group and within-group variations
… money, political concession, publicity, intimidation effects, among many others. However, notable violent political groups differ significantly in how frequently they engage in kidnappings. Meanwhile, even avid kidnapping groups show episodes of particularly high numbers of kidnappings committed …
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The Relationships Between Stress, Psychosocial Resources, and Mental Health and Adherence Outcomes among Perinatally HIV-Infected Adolescents in South Africa
<p>Adolescents living with perinatally acquired HIV (APHs) in sub-Saharan Africa (SSA) constitute a significant population group that is experiencing poor HIV treatment outcomes (CIPHER Global Cohort Collaboration, 2018). Compared to younger children and older adults within the SSA sub-region, APHs …