Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS WITH APPLICATION TO VIEWERSHIP OF MOTION PICTURES(2014) Tian, Yue; Smith, Paul; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Principal Component Analysis (PCA) is one widely used data processing technique in application, especially for dimensionality reduction. Functional Principal Component Analysis (fPCA) is a generalization of ordinary PCA, which focuses on a sample of functional observations and projects the original functional curves to a new space of orthogonal dimensions to capture the primary features of original functional curves. While, fPCA suffers from two potential error sources. One error source is originated from truncation when we approximate the functional subject's expansion; The other stems from estimation when we estimate the principal components from the sample. We first introduce a generalized functional linear regression model and propose it in the Quasi-likelihood setting. Asymptotic inference of the proposed functional regression model is developed. We also utilize the proposed model to help marketing operational decision process by analyzing viewership of motion pictures. We start with discussing customer reviews effect on movie box office sales. We use the functional regression model with function interactions to measure the effect of Word-of-Mouth on movie box office sales. One main challenge of modeling with functional interactions is the interpretation of model estimate results. We demonstrate one method to help us get important insights from model results by plotting and controlling a re-labbeld 3-D plot. Apart from movie performance in theater, we also employ functional regression model to predict movie pre-release demand in Video-on-Demand (VOD) channel. As its growing popularity, VOD market attracts much attention in marketing research. We analyze the prediction accuracy of our proposed functional regression model with spatial components and find that our proposed model gives us the best predictive accuracy. In summary, the dissertation develops asymptotic properties of a generalized functional linear regression model, and applies the proposed model in analyzing viewership of motion picture both in theater and Video-on-Demand channels. The proposed model not only advances our understanding of motion picture demand, but also helps optimize business decision making process.Item MANUFACTURING SECTOR PRODUCTIVITY IN INDIA: ALL INDIA TRENDS, REGIONAL PATTERNS, AND NETWORK EXTERNALITIES FROM INFRASTRUCTURE ON REGIONAL GROWTH(2010) Mohommad, Adil; Hulten, Charles R; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation I examine sources of growth in the formal manufacturing sector in India, from 1970 to 2003. I consider both all-India trends and state-level trends in the growth of resource efficiency, measured by TFP, and the relative contribution of TFP growth to output growth in manufacturing, as compared to capital accumulation. At the state level, I also examine the relationship between per-capita income and trends in output per worker and TFP in the manufacturing sector. Finally, in a spatial econometric framework, I test for the presence and magnitude of network spillovers from infrastructure, including national and state highways, and electricity generation capacity, on manufacturing TFP levels across states. My work contributes to an on-going debate on the response of manufacturing sector TFP to the implementation of economic reforms in India, in the 1980s and 1990s. At the regional level, this dissertation addresses not only the literature on the causes behind rising income inequality across states, but also on the role of infrastructure on regional growth, restricting attention to the manufacturing sector. The results of this dissertation show that at the all-India level and at the state level, manufacturing sector TFP growth accelerated in India during periods of economic reform. The contribution of TFP growth to output growth increased in the 1990s relative to earlier periods, and exceeded the contribution of capital accumulation. At the state level, I find evidence of convergence in growth rates of output per worker and TFP in manufacturing. I do not find evidence of a significant correlation between output per worker in manufacturing and state per-capita incomes. Given the relatively small share of the manufacturing sector in state GDP on average, these results imply that the source of rising income inequalities across states may not be manufacturing. Finally, I find some evidence to suggest that there exist positive network spillovers from physical infrastructure on manufacturing sector TFP. The results suggest that doubling the stock of national and state highways, and electricity generation capacity can lead to a nine percent increase in manufacturing sector output.