Sample size determination for logistic regression

Motrenko, Anastasiya; Strijov, Vadim; Weber, GERHARD

doi:10.1016/j.cam.2013.06.031

Sample size determination for logistic regression

Motrenko A., Strijov V., Weber G.

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, vol.255, pp.743-752, 2014 (SCI-Expanded, Scopus)

Publication Type: Article / Article
Volume: 255
Publication Date: 2014
Doi Number: 10.1016/j.cam.2013.06.031
Journal Name: JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Page Numbers: pp.743-752
Keywords: Logistic regression, Sample size, Feature selection, Bayesian inference, Kullback-Leibler divergence
Middle East Technical University Affiliated: Yes

Abstract

The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regression model parameters as a multivariate variable, propose to estimate the sample size using the distance between parameter distribution functions on cross-validated data sets. Herewith, the authors give a new contribution to data mining and statistical learning, supported by applied mathematics. (C) 2013 Elsevier B.V. All rights reserved.