A goodness-of fit improvement based on tau-preserving transformation for semiparametric family of copulas

Susam S. O., Hüdaverdi B.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.52, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52
  • Publication Date: 2023
  • Doi Number: 10.1080/03610926.2022.2052900
  • Journal Name: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Keywords: Semiparametric family of copulas, tau-preserving transform, goodness-of fit test
  • Dokuz Eylül University Affiliated: Yes

Abstract

In this paper, we study on improving the goodness-of fit for the data by using tau-preserving transform for the semiparametric family of bivariate copulas. We estimate the generator function phi using the Bezier polynomial function and define tau- preserving transformation on the generator function under positive quadrant dependence assumption. We investigate the performance of our methodology on real data example contained life expectancy study. The findings indicate that the model fitting is improved by using tau- preserving transformation.