Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Advancements in Small Area Estimation Using Hierarchical Bayesian Methods and Complex Survey Data(2024) Das, Soumojit; Lahiri, Partha; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses critical gaps in the estimation of multidimensional poverty measures for small areas and proposes innovative hierarchical Bayesian estimation techniques for finite population means in small areas. It also explores specialized applications of these methods for survey response variables with multiple categories. The dissertation presents a comprehensive review of relevant literature and methodologies, highlighting the importance of accurate estimation for evidence-based policymaking. In Chapter \ref{chap:2}, the focus is on the estimation of multidimensional poverty measures for small areas, filling an essential research gap. Using Bayesian methods, the dissertation demonstrates how multidimensional poverty rates and the relative contributions of different dimensions can be estimated for small areas. The proposed approach can be extended to various definitions of multidimensional poverty, including counting or fuzzy set methods. Chapter \ref{chap:3} introduces a novel hierarchical Bayesian estimation procedure for finite population means in small areas, integrating primary survey data with diverse sources, including social media data. The approach incorporates sample weights and factors influencing the outcome variable to reduce sampling informativeness. It demonstrates reduced sensitivity to model misspecifications and diminishes reliance on assumed models, making it versatile for various estimation challenges. In Chapter \ref{chap: 4}, the dissertation explores specialized applications for survey response variables with multiple categories, addressing the impact of biased or informative sampling on assumed models. It proposes methods for accommodating survey weights seamlessly within the modeling and estimation processes, conducting a comparative analysis with Multilevel Regression with Poststratification (MRP). The dissertation concludes by summarizing key findings and contributions from each chapter, emphasizing implications for evidence-based policymaking and outlining future research directions.Item Quality-Aware Data Source Management(2015) Rekatsinas, Theodoros; Deshpande, Amol; Getoor, Lise; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Data is becoming a commodity of tremendous value in many domains. The ease of collecting and publishing data has led to an upsurge in the number of available data sources --- sources that are highly heterogeneous in the domains they cover, the quality of data they provide, and the fees they charge for accessing their data. However, most existing data integration approaches, for combining information from a collection of sources, focus on facilitating integration itself but are agnostic to the actual utility or the quality of the integration result. These approaches do not optimize for the trade-off between the utility and the cost of integration to determine which sources are worth integrating. In this dissertation, I introduce a framework for quality-aware data source management. I define a collection of formal quality metrics for different types of data sources, including sources that provide both structured and unstructured data. I develop techniques to efficiently detect the content focus of a large number of diverse sources, to reason about their content changes over time and to formally compute the utility obtained when integrating subsets of them. I also design efficient algorithms with constant factor approximation guarantees for finding a set of sources that maximizes the utility of the integration result given a cost budget. Finally, I develop a prototype quality-aware data source management system and demonstrate the effectiveness of the developed techniques on real-world applications.