Moon, Haeun
(2022)
A New Test of Independence and Its Application to Variable Selection.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
In the first part of our research, we propose a new interpoint-ranking sign covariance
measure for nonparametric test of independence. The proposed method is applicable to
general types of random objects as long as a meaningful similarity measure can be defined,
and it is shown to be zero if and only if the two random variables are independent. The
test statistic is a U-statistic, whose large sample behavior guarantees that the proposed
test is consistent against general types of alternatives. Numerical experiments and data
analyses demonstrate the great empirical performance of the proposed method. In the second
part, we propose to combine the frequent voting idea with the proposed and existing
test of independence methods for model-free variable selection. This research is motivated
and illustrated by an application in selecting important genes related to suicidal behaviors.
Numerical experiments demonstrate nice empirical performance of the proposed method.
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Details
Item Type: |
University of Pittsburgh ETD
|
Status: |
Unpublished |
Creators/Authors: |
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ETD Committee: |
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Date: |
12 October 2022 |
Date Type: |
Publication |
Defense Date: |
25 April 2022 |
Approval Date: |
12 October 2022 |
Submission Date: |
4 August 2022 |
Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
Number of Pages: |
75 |
Institution: |
University of Pittsburgh |
Schools and Programs: |
Dietrich School of Arts and Sciences > Statistics |
Degree: |
PhD - Doctor of Philosophy |
Thesis Type: |
Doctoral Dissertation |
Refereed: |
Yes |
Uncontrolled Keywords: |
Consistent; Independence Test; Interpoint distance; Nonparametric; Sign Covariance;Model-free selection. |
Date Deposited: |
12 Oct 2022 15:25 |
Last Modified: |
12 Oct 2022 15:25 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/43500 |
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