Sample size determination for logistic regression

Date

2014-01-01

Author

Motrenko, Anastasiya
Strijov, Vadim
Weber, Gerhard Wilhelm

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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.

Subject Keywords

Applied Mathematics, Computational Mathematics

URI

https://hdl.handle.net/11511/57420

Journal

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

DOI

https://doi.org/10.1016/j.cam.2013.06.031

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

A. Motrenko, V. Strijov, and G. W. Weber, “Sample size determination for logistic regression,” JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, pp. 743–752, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57420.