• Statistical methods for complex spatial data. 

  Kim, Minho, 1988- (2021-07-12)

  Spatial analysis is an active research area as it allows us to solve problems containing geographic information in various applications. In this dissertation, we consider some challenging issues we often face in practice. ...

• Contributions to algebraic pattern recognition and integrated likelihood ratio confidence intervals. 

  Ma, Qida, 1990- (2021-04-12)

  Nonlinear polynomial equations describing positive-dimensional solution sets are called real varieties that in general may be quite complex but locally look like smooth manifolds. In the first chapter, we describe ways to ...

• Bayesian spatial misclassification model for areal count data with applications to COVID-19. 

  Chen, Jinjie, 1986- (2021-04-21)

  As of December 14, 2020, there have been more than 72.1 million confirmed cases, of which more than 1.61 million have died of COVID-19 globally. In the United States, there are more than 16,200,000 confirmed cases and ...

• Integrated-likelihood-ratio confidence intervals obtained from data via a double-sampling scenario. 

  Wiley, Briceon Curtis, 1994-; 0000-0003-2297-2006 (2020-08-26)

  Hypothesis testing has been a primary focus of statistical inference. Recently, confidence intervals (CIs) have been suggested as a superior inference form because of the additional information they provide to a scientist ...

• Multivariate fault detection and isolation. 

  Klanderman, Molly C., 1994-; 0000-0002-1129-1347 (2020-06-08)

  In a variety of industrial settings, many complexly related variables are monitored to ensure that a process remains in control (IC) over time. Faults that remain undetected can cause extensive damage that require costly ...

• Lehmann ROC regression and spatial classification. 

  Innerst, Melissa, 1993- (2019-12-17)

  Receiver Operating Curves (ROC) are a widely used measure of accuracy in diagnostic tests. Recently, there has been an increased interest in the effect that covariates have on the accuracy of the tests. As a result, several ...

• Detecting episodes of star formation using Bayesian model selection. 

  Lawler, Andrew Joseph, 1985-; 0000-0002-2826-7431 (2019-10-30)

  Bayesian model comparison is a data-driven method to establish model complexity. In this dissertation we investigate its use in detecting multiple episodes of star formation from the analysis of the Spectral Energy ...

• On testing for a difference in two high-dimensional mean vectors. 

  Worley, Whitney V., 1990-; 0000-0003-2672-1693 (2019-11-18)

  A common problem in multivariate statistical analysis involves testing for differences in the mean vectors from two populations with equal covariance matrices.This problem is considered well-posed when the sum of the two ...

• Bayesian adjustment for misclassification bias and prior elicitation for dependent parameters. 

  Lakshminarayanan, Divya Ranjani, 1993- (2019-11-25)

  This research is motivated by problems in biopharmaceutical research. Prior elicitation is defined as formulating an expert's beliefs about one or more uncertain quantities into a joint probability distribution, and is ...

• Bayesian inference for vaccine efficacy and prediction of survival probability in prime-boost vaccination regimes. 

  Lu, Yuelin, 1992- (2019-11-08)

  This dissertation consists of two major topics on applying Bayesian statistical methods in vaccine development. Chapter two concerns the estimation of vaccine efficacy from validation samples with selection bias. Since ...

• Computational Bayesian methods applied to complex problems in bio and astro statistics. 

  Elrod, Chris Michael, 1991- (2019-09-05)

  In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model ...

• Contributions to computational algebraic statistics. 

  Innerst, Grant Lee, 1993-; 0000-0002-0904-9877 (2019-07-17)

  The field of algebraic statistics arose from the realization that problems in statistics can often be viewed, and subsequently solved, using algebraic concepts and tools. In this work, we explore the application of algebraic ...

• Topic on the statistical analysis of high-dimensional data. 

  Stein, Caleb Karl, 1985- (2019-04-15)

  High-dimensional genomic data can provide deep insight into biological processes. However, conventional statistical methods typically cannot be applied directly to genomic data sets because the high dimensionality of markers ...

• Adaptive designs for phase II clinical trials with binary endpoints. 

  Carlile, Tom (, 2019-02-01)

  Because the sample size is varying while the estimate of sample size is changing, the quality of an approximation of the binomial by the Gaussian is variable and thus not desirable. Also, adaptive designs do not follow the ...

• A power contrast of tests for homogeneity of covariance matrices in a high-dimensional setting. 

  Barnard, Ben Joseph, 1987-; 0000-0002-1817-2295 (2018-10-31)

  Multivariate statistical analyses, such as linear discriminant analysis, MANOVA, and profile analysis, have a covariance-matrix homogeneity assumption. Until recently, homogeneity testing of covariance matrices was limited ...

• Bayesian approach to partially validated binary regression with response and exposure misclassification. 

  Anderson, Katrina Julie, 1987-; 0000-0002-3189-871X (2018-06-09)

  Misclassification of epidemiological and observational data is a problem that commonly arises and can have adverse ramifications on the validity of results if not properly handled. Considerable research has been conducted ...

• Weibull mixture model for grouped data and pattern identification in spatial and spatial-temporal data. 

  Yu, Youjiao, 1987-; 0000-0003-0519-9605 (2018-06-22)

  Motived by problems in geology, civil engineering, and material science, we develop statistical models and apply statistical tools for characterizing patterns that exist in different types of data. In the first project, ...

• On inferring cognitive impairment from a battery of tests and predicting an event-of-interest using longitudinal and time-to-event data. 

  McCreary, Morgan C., 1988- (2018-06-29)

  In this dissertation we first investigated two commonly used methods’ and a recently proposed method’s ability to predict conditional survival probabilities based on longitudinal biomarker measurements. Then, in preparation ...

• Bayesian propensity score analysis for clustered observational studies. 

  Zhou, Qi, 1990- (2018-04-12)

  There is increasing demand to investigate questions in observational study. The propensity score is a popular confounding adjustment technique to ensure valid causal inference for observational study. Observational data ...

• Beta regression for modeling a covariate-adjusted ROC. 

  Stanley, Sarah Elizabeth, 1992-; 0000-0002-0740-8688 (2018-03-12)

  The receiver operating characteristic (ROC) curve is a well-accepted measure of accuracy for diagnostic tests. In many applications, test performance is affected by covariates. As a result, several regression methodologies ...

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