Physical Sciences and Mathematics Commons^™

Towards Analytical Techniques For Optimizing Knowledge Acquisition, Processing, Propagation, And Use In Cyberinfrastructure, Leonardo Octavio Lerma

Open Access Theses & Dissertations

For many decades, there has been a continuous progress in science and engineering applications.

A large part of this progress comes from the new knowledge that researchers acquire, propagate, and use. This new knowledge has revolutionized many aspects of our life, from driving to communications to shopping.

Somewhat surprisingly, there is one area of human activity which is the least impacted by the modern technological progress: the very processes of acquiring, processing, and propagating information. When we decide where to place sensors, which algorithm to use for processing the data – we rely mostly on our own intuition and on …

Bayesian Adaptive Penalized Splines In Nonparametric Regression And In Spectral Time Series Analysis, Luis Angel Mora

Open Access Theses & Dissertations

A Bayesian approach to nonparametric regression using Penalized splines (P-splines) is presented. The approach uses the linear mixed model formulation of P-spines. The usual model assumes a single value for the smoothing parameter controlling the amount of smoothing of the fitted function. The main focus of the Thesis is on spatially adaptive smoothing where the smoothing parameter is a function of the covariate so that different amounts of smoothing are applied in different regions of the covariate. An application to spectral time series analysis will be demonstrated. Markov chain Monte Carlo methods are used to make inference based on the …

Pre-Tuned Ridge Regression And Its Extension To Generalized Linear Models, Yaa Tawiah Wonkye

Open Access Theses & Dissertations

Ridge regression is regularization or shrinkage method and a common approach in dealing with multicollinearity in conventional regression analysis. Ridge regression is widely used by statistical analyst since it is one of the best compared to other regularization methods. Also, the introduction of high dimension and ultra-high dimensional data has become an issue of concern and ridge regression is one way of dealing with such data. One of the key issues associated with ridge regression is the determination of the tuning or ridge parameter. The common practice is to fit ridge regression for a different number of values of tuning …

Combining Semiparametric Regression And Kriging For Prediction Of Pm2.5 Pollutant Levels At Unmonitored Locations With Meterological And Traffic Data, Justin Jonathan Strate

Open Access Theses & Dissertations

Particulate matter (PM) is defined by the Texas Commission on Environmental Quality (TCEQ) as "a mixture of solid particles and liquid droplets found in the air". These particles vary widely in size. Those particles that are less than 2.5 micrometers in aerodynamic diameter are known as Particulate Matter 2.5 or PM2.5. These particles are inhaled, and their health effects are still largely being studied. Past studies have assessed PM2.5 exposure of a population, yet individual exposure is more diffcult to assess and may vary widely in a population. Recent studies have combined semiparametric models with kriging (Li et. al [2012]) …

A Bayes Approach In Step-Stress Accelerated Life Testings, Hao Yang Teng

Open Access Theses & Dissertations

A Bayesian analysis for the Weibull proportional hazard (PH) model is presented. A comparison between the Weibull PH model and the Weibull cumulative exposure (CE) model is made graphically and mathematically. The PH model is as flexible as the CE model in fitting step-stress data and the mathematical form of the PH model enables researchers to do Bayesian inferencemuch easier than the CE model. In addition, the PH model has the desirable proportional hazard property. A convex tent prior is used for Bayesian analysis. Markov chain Monte Carlo methods are used for posterior inferences. In this study, we adopt two …