Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 97-98). Also available on the Internet.

Thesis (Ph.D.)--Ohio University, June, 2009. / Includes bibliographical references (leaves 146-149)

Thesis (Ph.D.)--Ohio University, June, 2009. / Title from PDF t.p. Includes bibliographical references (leaves 146-149)

Thesis (Ph. D. in Mathematics)--Vanderbilt University, 2003. / Title from PDF title screen. Includes bibliographical references and index.

Multivariate ordinal response data, such as severity of pain, degree of disability, and satisfaction with a healthcare provider, are prevalent in many areas of research including public health, biomedical, and social science research. Ignoring the multivariate features of the response variables, that is, by not taking the correlation between the errors across models into account, may lead to substantially biased estimates and inference. In addition, such multivariate ordinal outcomes frequently exhibit a high percentage of zeros (zero inflation) at the lower end of the ordinal scales, as compared to what is expected under a multivariate ordinal distribution. Thus, zero inflation coupled with the multivariate structure make it difficult to analyze such data and properly interpret the results. Methods that have been developed to address the zero-inflated data are limited to univariate-logit or univariate-probit model, and extension to bivariate (or multivariate) probit models has been very limited to date. In this research, a latent variable approach was used to develop a Mixture Bivariate Zero-Inflated Ordered Probit (MBZIOP) model. A Bayesian MCMC technique was used for parameter estimation. A simulation study was then conducted to compare the performances of the estimators of the proposed model with two existing models. The simulation study suggested that for data with at least a moderate proportion of zeros in bivariate responses, the proposed model performed better than the comparison models both in terms of lower bias and greater accuracy (RMSE). Finally, the proposed method was illustrated with a publicly-available drug-abuse dataset to identify highly probable predictors of: (i) being a user/nonuser of marijuana, cocaine, or both; and (ii), conditional on user status, the level of consumption of these drugs. The results from the analysis suggested that older individuals, smokers, and people with a prior criminal background have a higher risk of being a marijuana only user, or being the user of both drugs. However, cocaine only users were predicted on the basis of being younger and having been engaged in the criminal-justice system. Given that an individual is a user of marijuana only, or user of both drugs, age appears to have an inverse effect on the latent level of consumption of marijuana as well as cocaine. Similarly, given that a respondent is a user of cocaine only, all covariates--age, involvement in criminal activities, and being of black race--are strong predictors of the level of cocaine consumption. The finding of older age being associated with higher drug consumption may represent a survival bias whereby previous younger users with high consumption may have been at elevated risk of premature mortality. Finally, the analysis indicated that blacks are likely to use less marijuana, but have a higher latent level of cocaine given that they are user of both drugs.

The current study contributes to the already substantial scholarly literature on telecommuting by estimating a joint model of three dimensions- option, choice and frequency of telecommuting. In doing so, we focus on workers who are not self-employed workers and who have a primary work place that is outside their homes. The unique methodological features of this study include the use of a general and flexible generalized hurdle count model to analyze the precise count of telecommuting days per month, and the formulation and estimation of a model system that embeds the count model within a larger multivariate choice framework. The unique substantive aspects of this study include the consideration of the "option to telecommute" dimension and the consideration of a host of residential neighborhood built environment variables. The 2009 NHTS data is used for the analysis, and allows us to develop a current perspective of the process driving telecommuting decisions. This data set is supplemented with a built environment data base to capture the effects of demographic, work-related, and built environment measures on the telecommuting-related dimensions. In addition to providing important insights for policy analysis, the results in this study indicate that ignoring the "option" dimension of telecommuting can, and generally will, lead to incorrect conclusions regarding the behavioral processes governing telecommuting decisions. The empirical results have implications for transportation planning analysis as well as for the worker recruitment/retention and productivity literature. / text

This thesis proposes an estimation approach for count data models with endogenous covariates. The maximum approximate composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Irving, Texas. The sample is drawn from the Texas Department of Transportation crash incident files for the year 2008. The results highlight the importance of accommodating endogeneity effects in count models. In addition, the results reveal the increased propensity for crashes at intersections with flashing lights, intersections with crest approaches, and intersections that are on frontage roads. / text

This study assesses the role of crossing locations and light conditions in pedestrian injury severity through a multivariate regression analysis to control for many other factors that also may influence pedestrian injury severity. Crossing locations include midblock and intersections, and light conditions include daylight, dark with street lighting, and dark without street lighting. The study formulates a theoretical framework on the determinants of pedestrian injury severity, and specifies an empirical model accordingly. An ordered probit model is then applied to the KABCO severity scale of pedestrian injuries which occurred while attempting street crossing in the years 1986 to 2003 in Florida. In terms of crossing locations, the probability of a pedestrian dying when struck by a vehicle, is higher at midblock locations than at intersections for any light condition. In fact, the odds of sustaining a fatal injury is 49 percent lower at intersections than at midblock locations under daylight conditions, 24 percent lower under dark with street lighting conditions, and 5 percent lower under dark without street lighting conditions. Relative to dark conditions without street lighting, daylight reduces the odds of a fatal injury by 75 percent at midblock locations and by 83 percent at intersections, while street lighting reduces the odds by 42 percent at midblock locations and by 54 percent at intersections.

Obesity, the second leading cause of preventable death in the U.S., and related health problems increase people’s concerns about healthy food consumption. The increased prevalence of obesity is a major concern of societies both in developed and developing countries. Nutrition label usage has been increasing due to the link between diet and health. This study intends to provide a framework for describing profiles of consumers who are more likely to use nutrition labels in USA and Turkey, a developing country with increasing obesity rates in recent years. Empirical results present similarities and differences between consumers’ attributes for food label usage in two countries. The main contribution of this study is to investigate the relationship between the importance of serving size, while the number of expanded portion sized products in the market is increasing, and rising obesity rates. Ordered probit model analysis is used to identify the effects of demographics, health status and other components of the nutrition facts panel on selected dependent variables. Better understanding consumers’ responses to nutrition labels may guide consumers and manufacturers to broaden the communication channels through nutrition labels. The findings of this study can provide useful information to policy makers, agribusinesses, manufacturers and marketing professionals.

Food safety events receive substantial media coverage and can create devastating economics losses for agribusiness firms. It is unclear what factors influence consumers’ purchasing decisions before or after a food safety event occurs. The objectives of this study is to identify these factors that influence purchasing decisions, determine how consumers respond to hypothetical food safety events, and compare these findings across different products and geographical regions. The data for this research was obtained from two surveys. One survey concerned fresh produce while the second focused on meat products. The SPARTA model, based on the Theory of Planned Behavior, is used to determine the impact of probable factors that influence consumers’ purchasing decisions. The result of this research suggests that consumers have clearly-defined levels of trust regarding sources of food safety information. In general, a food safety event occurring in the fresh produce market seems to affect purchasing decisions more than the same event occurring in the meat market. Comparison of findings across geographical regions is less clear. Agribusiness firms can use these results to form a base strategic response plan for food safety events.