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Kissel, Nicholas (2019) CHARACTERIZING UNCERTAINTY IN LOW-DIMENSIONAL MODEL SELECTION. Master's Thesis, University of Pittsburgh. (Unpublished)

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In the context of big and often high-dimensional data, valid procedures for assessing variable importance and identifying accurate model representations are essential tools, especially in the presence of substantial instability. Instead of seeking to find only a single set of covariates that form the empirically optimal model, we propose an automated procedure for identifying an entire collection of stable and predictively similar models. Within each iterate of the selection method, we develop a procedure to identify covariates that are predictively similar with regard to a chosen loss function, thereby providing multiple options as to which covariate should be added to the final model. By construction, our procedure acts a wrapper method that can be applied to any statistical or machine learning technique. Furthermore, we provide a natural and intuitive graphical display of these model paths that makes apparent potential underlying relationships between covariates as well as the relative importance of the covariates selected.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kissel, Nicholasnjk51@pitt.edunjk51
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMentch, Lucaslkm31@pitt.edulkm31
Committee MemberIyengar, Satishssi@pitt.edussi
Committee MemberCheng, Yuyucheng@pitt.eduyucheng
Date: 19 June 2019
Date Type: Publication
Defense Date: 5 April 2019
Approval Date: 19 June 2019
Submission Date: 12 April 2019
Access Restriction: 3 year -- Restrict access to University of Pittsburgh for a period of 3 years.
Number of Pages: 43
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Model Selection, Forward Selection, Stability Selection
Date Deposited: 19 Jun 2019 20:27
Last Modified: 19 Jun 2022 05:15

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