This dissertation investigates two methodological problems. The first problem concerns developing and optimizing multicomponent interventions. The traditional approach to this problem is to conduct a two-group randomized trial of a "likelybest" intervention vs. control, followed by observational...

Generalized linear models with covariate measurement error can be estimated by maximum likelihood using gllamm, a program that fits a large class of multilevel latent variable models (Rabe-Hesketh, Skrondal, and Pickles 2004). The program uses adaptive quadrature to evaluate the log likelihood,...

Item Response Theory (IRT) models are the basis of modern educational measurement. In order to increase testing efficiency, modern tests make ample use of groups of questions associated with a single stimulus (testlets). This violates the IRT assumption of local independence. However, a set of...

The point source is the most common type of source to be modeled for its effect on air pollution. Point sources provide auxiliary information that may impact both the mean and covariance structure of measured responses, but these possible impacts are often overlooked by spatial modelers. In this...

In this paper, I develop and estimate a dynamic model of strategicnetwork formation with heterogeneous agents. The main theoretical resultis the existence of a unique stationary equilibrium, which characterizesthe probability of observing a specific network in the data. As aconsequence, the...

Univariate hierarchical Bayes models are being vigorously researched for use in disease mapping, engineering, geology, and ecology. This dissertation shows how the models can also be used to build modelbased risk maps for areabased roadway traffic crashes. Countylevel vehicle crash records and...

A large body of literature studies the issues of the option price and other ex-antewelfare measures under the microeconomic theory to valuate reductions of risks inherentin environment and human health. However, it does not offer a careful discussion of howto estimate risk reduction values using...

This thesis develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. Our model has some desirable features. First, the number of regimes is not fixed and...

The present dissertation consists of three stand-alone research papers that all deal with factor models from a Bayesian perspective, both in a theoretical and an empirical setup. More precisely, the thesis is organized in a progressive way as follows: Chapter 1 briefly presents the general...

Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient methods for Bayesian inference for simultaneous estimation of latent...