Logistic regression with brownian-like predictors

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

26 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1575-1585
Journal / PublicationJournal of the American Statistical Association
Volume104
Issue number488
Publication statusPublished - Dec 2009
Externally publishedYes

Abstract

This article introduces a new type of logistic regression model involving functional predictors of binary responses, and provides an extension of this approach to generalized linear models. The predictors are trajectories that have certain sample path properties in common with Brownian motion. Time points are treated as parameters of interest, and confidence intervals are developed under prospective and retrospective (case-control) sampling designs. In an application to functional magnetic resonance imaging data, signals from individual subjects are used to find the portion of the time course that is most predictive of the response. This allows the identification of sensitive time points specific to a brain region and associated with a certain task, which can be used to distinguish between responses. A second application concerns gene expression data in a case-control study involving breast cancer, where the aim is to identify genetic loci along a chromosome that best discriminate between cases and controls. © 2009 American Statistical Association.

Research Area(s)

  • Brownian motion, Empirical process, Functional logistic regression, Functional magnetic resonance imaging, Gene expression, Lasso, M-estimation

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.

Citation Format(s)

Logistic regression with brownian-like predictors. / Lindquist, Martin A.; Mckeague, Ian W.
In: Journal of the American Statistical Association, Vol. 104, No. 488, 12.2009, p. 1575-1585.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review