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Articles 1 - 5 of 5
Full-Text Articles in Statistical Models
Semiparametric And Nonparametric Methods For Comparing Biomarker Levels Between Groups, Yuntong Li
Semiparametric And Nonparametric Methods For Comparing Biomarker Levels Between Groups, Yuntong Li
Theses and Dissertations--Statistics
Comparing the distribution of biomarker measurements between two groups under either an unpaired or paired design is a common goal in many biomarker studies. However, analyzing biomarker data is sometimes challenging because the data may not be normally distributed and contain a large fraction of zero values or missing values. Although several statistical methods have been proposed, they either require data normality assumption, or are inefficient. We proposed a novel two-part semiparametric method for data under an unpaired setting and a nonparametric method for data under a paired setting. The semiparametric method considers a two-part model, a logistic regression for …
Estimation Of The Treatment Effect With Bayesian Adjustment For Covariates, Li Xu
Estimation Of The Treatment Effect With Bayesian Adjustment For Covariates, Li Xu
Theses and Dissertations--Statistics
The Bayesian adjustment for confounding (BAC) is a Bayesian model averaging method to select and adjust for confounding factors when evaluating the average causal effect of an exposure on a certain outcome. We extend the BAC method to time-to-event outcomes. Specifically, the posterior distribution of the exposure effect on a time-to-event outcome is calculated as a weighted average of posterior distributions from a number of candidate proportional hazards models, weighing each model by its ability to adjust for confounding factors. The Bayesian Information Criterion based on the partial likelihood is used to compare different models and approximate the Bayes factor. …
Nonparametric Tests Of Lack Of Fit For Multivariate Data, Yan Xu
Nonparametric Tests Of Lack Of Fit For Multivariate Data, Yan Xu
Theses and Dissertations--Statistics
A common problem in regression analysis (linear or nonlinear) is assessing the lack-of-fit. Existing methods make parametric or semi-parametric assumptions to model the conditional mean or covariance matrices. In this dissertation, we propose fully nonparametric methods that make only additive error assumptions. Our nonparametric approach relies on ideas from nonparametric smoothing to reduce the test of association (lack-of-fit) problem into a nonparametric multivariate analysis of variance. A major problem that arises in this approach is that the key assumptions of independence and constant covariance matrix among the groups will be violated. As a result, the standard asymptotic theory is not …
Statistical Intervals For Various Distributions Based On Different Inference Methods, Yixuan Zou
Statistical Intervals For Various Distributions Based On Different Inference Methods, Yixuan Zou
Theses and Dissertations--Statistics
Statistical intervals (e.g., confidence, prediction, or tolerance) are widely used to quantify uncertainty, but complex settings can create challenges to obtain such intervals that possess the desired properties. My thesis will address diverse data settings and approaches that are shown empirically to have good performance. We first introduce a focused treatment on using a single-layer bootstrap calibration to improve the coverage probabilities of two-sided parametric tolerance intervals for non-normal distributions. We then turn to zero-inflated data, which are commonly found in, among other areas, pharmaceutical and quality control applications. However, the inference problem often becomes difficult in the presence of …
Bayesian Kinetic Modeling For Tracer-Based Metabolomic Data, Xu Zhang
Bayesian Kinetic Modeling For Tracer-Based Metabolomic Data, Xu Zhang
Theses and Dissertations--Statistics
Kinetic modeling of the time dependence of metabolite concentrations including the unstable isotope labeled species is an important approach to simulate metabolic pathway dynamics. It is also essential for quantitative metabolic flux analysis using tracer data. However, as the metabolic networks are complex including extensive compartmentation and interconnections, the parameter estimation for enzymes that catalyze individual reactions needed for kinetic modeling is challenging. As the pa- rameter space is large and multi-dimensional while kinetic data are comparatively sparse, the estimation procedure (especially the point estimation methods) often en- counters multiple local maximum such that standard maximum likelihood methods may yield …