Topics in high-dimensional statistical learning

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Our research explores three topics in high-dimensional statistical learning. First, we consider a regression scenario where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of l1 -regularized regression for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. We illustrate this idea on real and simulated data. We then consider regression scenarios where it is natural to allow coefficients to vary as smooth functions of other variables. We propose two constrained versions for this problem and show how to solve them efficiently. Last, we study canonical correlation analysis (CCA) in high-dimensional settings and propose a sparse CCA framework, and provide two efficient algorithms. We discuss links between CCA and linear discriminant analysis (LDA). We demonstrate its use on real and simulated data.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2018; ©2018
Publication date 2018; 2018
Issuance monographic
Language English


Author Suo, Xiaotong
Degree supervisor Saunders, Michael A
Degree supervisor Tibshirani, Robert
Thesis advisor Saunders, Michael A
Thesis advisor Tibshirani, Robert
Thesis advisor Taylor, Jonathan E
Degree committee member Taylor, Jonathan E
Associated with Stanford University, Institute for Computational and Mathematical Engineering.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Xiaotong Suo.
Note Submitted to the Institute for Computational and Mathematical Engineering.
Thesis Thesis Ph.D. Stanford University 2018.
Location electronic resource

Access conditions

© 2018 by Xiaotong Suo
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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