PhD, University of Cincinnati, 2018, Arts and Sciences: Mathematical Sciences

Nonparametric modeling tends to capture the underlying structures in the data without imposing strong model assumptions. The nonparametric estimation provides powerful data-driven approaches to fit a flexible model to the data. Single-index models are useful and appealing tools to preserve the flexibility and interpretability but to overcome “curse of dimensionality” problems in nonparametric regression. In this dissertation, we consider partially linear single-index models for quantile regression. This set of semi-parametric models allow some of covariates in linear form and other covariates in nonparametric term to reflect the non-linear feature in modeling the conditional quantiles of the response variable. We first develop efficient estimation and variable selection for partially linear single-index quantile models in the fixed dimension. We use spline smoothing with B-spline basis to estimate the nonparametric component and adopt the non-convex penalties to select variables simultaneously. We study the theoretical properties of the resulting estimators and establish the “oracle property” for penalized estimation. With the rise of new technologies used in data collection and storage, high dimensional data spring up and become available in various scientific fields. Often researchers face the new challenge that the dimension of the explanatory variables, p, may increase with the sample size, n, or potentially become much larger than n. For those problems of high to ultra-high dimensionality, data are likely to be heterogeneous and the underlying model is prone to be nonlinear. Variable selection will also play a critical role in the dimension reduction and modeling process. Thus, we propose a penalized estimation under the sparsity assumption for partially linear single-index quantile models in high dimension. We further investigate ultra-high dimensional penalized partially linear single-index quantile models in which both linear components and single-index vari (open full item for complete abstract)

Committee: Dan Ralescu Ph.D. (Committee Chair); Yan Yu Ph.D. (Committee Chair); Emily Kang Ph.D. (Committee Member); Ju-Yi Yen (Committee Member) Subjects: Statistics

Master of Science (MS), Ohio University, 2013, Civil Engineering (Engineering and Technology)

In this thesis, road safety assessment and prediction modeling for U.S. states fatal crashes are addressed. In the first part, a DEA-based Malmquist Index model was developed to assess the relative efficiency and productivity of U.S. states in decreasing the number of road fatalities. Even though the national trend in fatal crashes has reached to the lowest level since 1949 (Traffic Safety Annual Assessment Highlights, 2010), a state-by-state analysis and comparison has not been studied considering other characteristics of the holistic national road safety assessment problem in any work in the literature or organizational reports. The single output, fatal crashes, and five inputs were aggregated into single road safety score and utilized in the DEA-based Malmquist Index mathematical model. The period of 2002-2008 was considered due to data availability for the inputs and the output considered. According to the results, there is a slight negative productivity (an average of -0.2 percent productivity) observed in the U.S. on minimizing the number of fatal crashes along with an average of 2.1 percent efficiency decline and 1.8 percent technological improvement. The productivity in reducing the fatal crashes can only be attributed to the technological growth since there is a negative efficiency growth is occurred. It can be concluded that even though there is a declining trend observed in the fatality rates, the efficiency of states in utilizing societal and economical resources towards the goal of zero fatality is not still efficient. In the second part, a nonparametric prediction model, Artificial Neural Network, was developed to assist policy makers in minimizing fatal crashes across the United States. Seven input variables from four safety performance input domains while fatal crashes was utilized as the single output variable for the scope of the research. Artificial Neural Networks (ANN) was utilized and the best neural network model was developed out of 1000 n (open full item for complete abstract)

Committee: Deborah McAvoy Ph.D. (Advisor); Byung-Cheol Kim Ph.D. (Committee Member); Ken Walsh Ph.D. (Committee Member); M. Khurrum S. Bhutta Ph.D. (Committee Member) Subjects: Civil Engineering; Industrial Engineering; Transportation

Doctor of Philosophy, The Ohio State University, 2012, Agricultural, Environmental and Developmental Economics

For many decades, the relationship between urban and rural places was well understood. Beginning in the early 20th century, however, this distinct dichotomy broke down as large numbers of businesses and people migrated out of the central city. As a result of this expansion of the urban center and the growth in suburban and exurban development, many urban fringe areas in the U.S. have become characterized by low-density and fragmented development. As a result of these changing land use patterns and the potential for both positive and negative outcomes, researchers and policymakers have become interested in understanding both the demand-side and supply-side incentive mechanisms that have led to this type of fragmented development. The objective of this research is to fill several gaps in the empirical literature on residential land conversion and land use policy by using unique micro-level data on historical subdivision development, land conversion, the platting and subdivision approval process, house prices and policy changes. In our first essay, we build on the growing literature that looks at the effect of regulation on housing supply decisions and focus specifically on the question of whether the expected time to completion affects both the decision to develop as well as the quantity of lots chosen by the individual landowners. Using a unique micro dataset on the timing of subdivision approvals by a local planning agency and a sample selection Poisson model, we test the effects of implicit costs that arise from uncertain subdivision approval on the timing, quantity and pattern of residential subdivision development. Consistent with theory, we find that these regulation-induced implicit costs reduce the probability and size of subdivision development on any given parcel. Our results contribute to the growing supply-side literature on housing and land use, and provide a new explanation of scattered residential development as the outcome of heterogeneous regulatory c (open full item for complete abstract)

Committee: Elena Irwin PhD (Advisor); Mark Partridge PhD (Committee Member); Abdoul Sam PhD (Committee Member) Subjects: Agricultural Economics; Economics; Environmental Economics; Public Policy; Urban Planning