@article{High-Dimensional:2593,
      recid = {2593},
      author = {Liu, Haoyang},
      title = {Algorithmic and Statistical Optimality for  High-Dimensional Data},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2020-08},
      pages = {89},
      abstract = {For high-dimensional data, two of the most important  questions are the question of algorithmic optimality, which  asks for the optimal algorithm within a certain class of  computationally feasible procedures, and the question of  statistical optimality, which asks for the optimal  statistical procedure under a generating model. In this  thesis the question of algorithmic optimality is  investigated for the class of iterative thresholding  algorithms on sparse and low rank structures under the  framework of restricted optimality. The question of  statistical optimality is investigated for the  high-dimensional sparse changepoint detection problem and  the contaminated density estimation problem under the  minimax framework.},
      url = {http://knowledge.uchicago.edu/record/2593},
      doi = {https://doi.org/10.6082/uchicago.2593},
}