Skip to main content
Kent Academic Repository

Empirical Bayes logistic regression

Strimenopoulou, Foteini, Brown, Philip J. (2008) Empirical Bayes logistic regression. Statistical Applications in Genetics and Molecular Biology, 7 (2). ISSN 1544-6115. (doi:10.2202/1544-6115.1359) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:8191)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.2202/1544-6115.1359

Abstract

We construct a diagnostic predictor for patient disease status based

on a single data set of mass spectra of serum samples together with

the binary case-control response. The model is logistic regression

with Bernoulli log-likelihood augmented either by quadratic ridge

or absolute $L_1$ penalties. For ridge penalization using the

singular value decomposition we reduce the the number of variables

for maximization to the rank of the design matrix. With

log-likelihood loss, 10-fold cross-validatory choice is employed to

specify the penalization hyperparameter. Predictive ability is

judged on a set-aside subset of the data.

Item Type: Article
DOI/Identification number: 10.2202/1544-6115.1359
Additional information: Article No 9
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Philip Brown
Date Deposited: 07 Mar 2009 13:00 UTC
Last Modified: 16 Nov 2021 09:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/8191 (The current URI for this page, for reference purposes)

University of Kent Author Information

Brown, Philip J..

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.