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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Some Dimension Reduction Strategies For The Analysis Of Survey Data, Jiaying Weng, Derek S. Young
Some Dimension Reduction Strategies For The Analysis Of Survey Data, Jiaying Weng, Derek S. Young
Statistics Faculty Publications
In the era of big data, researchers interested in developing statistical models are challenged with how to achieve parsimony. Usually, some sort of dimension reduction strategy is employed. Classic strategies are often in the form of traditional inference procedures, such as hypothesis testing; however, the increase in computing capabilities has led to the development of more sophisticated methods. In particular, sufficient dimension reduction has emerged as an area of broad and current interest. While these types of dimension reduction strategies have been employed for numerous data problems, they are scantly discussed in the context of analyzing survey data. This …
An Exploratory Statistical Method For Finding Interactions In A Large Dataset With An Application Toward Periodontal Diseases, Joshua Lambert
An Exploratory Statistical Method For Finding Interactions In A Large Dataset With An Application Toward Periodontal Diseases, Joshua Lambert
Theses and Dissertations--Epidemiology and Biostatistics
It is estimated that Periodontal Diseases effects up to 90% of the adult population. Given the complexity of the host environment, many factors contribute to expression of the disease. Age, Gender, Socioeconomic Status, Smoking Status, and Race/Ethnicity are all known risk factors, as well as a handful of known comorbidities. Certain vitamins and minerals have been shown to be protective for the disease, while some toxins and chemicals have been associated with an increased prevalence. The role of toxins, chemicals, vitamins, and minerals in relation to disease is believed to be complex and potentially modified by known risk factors. A …
Improving The Computational Efficiency In Bayesian Fitting Of Cormack-Jolly-Seber Models With Individual, Continuous, Time-Varying Covariates, Woodrow Burchett
Improving The Computational Efficiency In Bayesian Fitting Of Cormack-Jolly-Seber Models With Individual, Continuous, Time-Varying Covariates, Woodrow Burchett
Theses and Dissertations--Statistics
The extension of the CJS model to include individual, continuous, time-varying covariates relies on the estimation of covariate values on occasions on which individuals were not captured. Fitting this model in a Bayesian framework typically involves the implementation of a Markov chain Monte Carlo (MCMC) algorithm, such as a Gibbs sampler, to sample from the posterior distribution. For large data sets with many missing covariate values that must be estimated, this creates a computational issue, as each iteration of the MCMC algorithm requires sampling from the full conditional distributions of each missing covariate value. This dissertation examines two solutions to …
Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu
Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu
Theses and Dissertations--Statistics
Firstly, we reviewed some popular nonparameteric regression methods during the past several decades. Then we extended the compound estimation (Charnigo and Srinivasan [2011]) to adapt random design points and heteroskedasticity and proposed a modified Cp criteria for tuning parameter selection. Moreover, we developed a DCp criteria for tuning paramter selection problem in general nonparametric derivative estimation. This extends GCp criteria in Charnigo, Hall and Srinivasan [2011] with random design points and heteroskedasticity. Next, we proposed a change point detection method via compound estimation for both fixed design and random design case, the adaptation of heteroskedasticity was considered for the method. …