Empirical Bayes logistic regression

Strimenopoulou, F. and Brown, P.J. (2008) Empirical Bayes logistic regression. Statistical Applications in Genetics and Molecular Biology, 7 (2). ISSN 1544-6115 . (The full text of this publication is not available from this repository)

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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
Additional information: Article No 9
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Philip J Brown
Date Deposited: 07 Mar 2009 13:00
Last Modified: 14 Jan 2010 14:30
Resource URI: http://kar.kent.ac.uk/id/eprint/8191 (The current URI for this page, for reference purposes)
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