Determination of model fitting with power-divergence-type measure of departure from symmetry for sparse and non-sparse square contingency tables

Altun G., SARAÇBAŞI T.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.51, no.7, pp.4087-4111, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 7
  • Publication Date: 2022
  • Doi Number: 10.1080/03610918.2020.1736301
  • Journal Name: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.4087-4111
  • Keywords: Measure of departure, Power-divergence family, Square contingency tables, Symmetry model, STATISTICS
  • Hacettepe University Affiliated: No

Abstract

In this study, we propose a gradation to decide the model fitting under the symmetry model by using the departure measure. An extensive simulation study under different parameter settings and scenarios is conducted to introduce a gradation on unit interval. The proposed gradation provides an opportunity to decide the model fitting under the symmetry model when the chi-squared assumption does not hold for small sample sizes. The usefulness of proposed gradation is proved empirically using four applications to real data sets.