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Andishe 2015, 20(1): 21-27 Back to browse issues page
Sample size determination for logistic regression
Abstract:   (5998 Views)

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 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.

Keywords: Logistic regression, Sample size, Feature selection, Bayesian inference, Kullback-Leibler divergence.
Full-Text [PDF 223 kb]   (5017 Downloads)    
Type of Study: Research | Subject: Special
Received: 2013/12/31 | Accepted: 2016/03/15 | Published: 2016/03/15
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Sample size determination for logistic regression. Andishe 2015; 20 (1) :21-27
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Volume 20, Issue 1 (4-2015) Back to browse issues page
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