Physical Sciences and Mathematics Commons^™

Statistical Modeling Of Carbon Dioxide And Cluster Analysis Of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, And Multi-Level Time Series Clustering, Doo Young Kim

USF Tampa Graduate Theses and Dissertations

The current study consists of three major parts. Statistical modeling, the connection between statistical modeling and cluster analysis, and proposing new methods to cluster time dependent information.

First, we perform a statistical modeling of the Carbon Dioxide (CO₂) emission in South Korea in order to identify the attributable variables including interaction effects. One of the hot issues in the earth in 21st century is Global warming which is caused by the marriage between atmospheric temperature and CO₂ in the atmosphere. When we confront this global problem, we first need to verify what causes the problem then we …

Statistical Learning With Artificial Neural Network Applied To Health And Environmental Data, Taysseer Sharaf

USF Tampa Graduate Theses and Dissertations

The current study illustrates the utilization of artificial neural network in statistical methodology. More specifically in survival analysis and time series analysis, where both holds an important and wide use in many applications in our real life. We start our discussion by utilizing artificial neural network in survival analysis. In literature there exist two important methodology of utilizing artificial neural network in survival analysis based on discrete survival time method. We illustrate the idea of discrete survival time method and show how one can estimate the discrete model using artificial neural network. We present a comparison between the two methodology …

Trend Analysis And Modeling Of Health And Environmental Data: Joinpoint And Functional Approach, Ram C. Kafle

USF Tampa Graduate Theses and Dissertations

The present study is divided into two parts: the first is on developing the statistical analysis and modeling of mortality (or incidence) trends using Bayesian joinpoint regression and the second is on fitting differential equations from time series data to derive the rate of change of carbon dioxide in the atmosphere.

Joinpoint regression model identifies significant changes in the trends of the incidence, mortality, and survival of a specific disease in a given population. Bayesian approach of joinpoint regression is widely used in modeling statistical data to identify the points in the trend where the significant changes occur. The purpose …