Statistical Machine Learning Methods for Complex, Heterogeneous Data

Mahtiyar Bonakdarpour
This thesis develops statistical machine learning methodology for three distinct tasks. Each method blends classical statistical approaches with machine learning methods to provide principled solutions to problems with complex, heterogeneous datasets. The first framework proposes two methods for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The second method provides a nonparametric approach to the econometric analysis of discrete choice. This method provides a...
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