@article{StatisticalMachineLearningMethodsforComplex:1788,
      recid = {1788},
      author = {Bonakdarpour, Mahtiyar},
      title = {Statistical Machine Learning Methods for Complex,  Heterogeneous Data},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2019-06},
      pages = {92},
      abstract = {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 scalable algorithm  for estimating utility functions with random forests, and  combines this with random effects to properly model  preference heterogeneity. The final method draws  inspiration from early work in statistical machine  translation to construct embeddings for variable-length  objects like mathematical equations.},
      url = {http://knowledge.uchicago.edu/record/1788},
      doi = {https://doi.org/10.6082/uchicago.1788},
}