Abstract

The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorical data analysis, in particular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

Keywords

Association coefficient; Bootstrap method; Chi-squared test; Contingency table; Monte Carlo simulation

Published in

Computer Methods and Programs in Biomedicine
2011, Volume: 104, number: 2, pages: 182-187
Publisher: ELSEVIER IRELAND LTD

SLU Authors

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Computer Science


Publication identifier

DOI: https://doi.org/10.1016/j.cmpb.2011.01.007

Permanent link to this page (URI)

https://res.slu.se/id/publ/46177