One of the most important assumptions in multiple regression analysis is the independence of the explanatory variables, however, this assumption is violated in several situations. In this work, we investigate regression equations when this independence does not hold and the explanatory variables are connected by many of elliptical copulas. We apply the proposed regression equation to study its heteroscedasticity diagnostic and using simulated data we also assess our regression model. A cross-validation procedure is carried out to ensure the unbiasedness of the results. Also, a real data analysis is presented as an application.
The work of the third author was supported by the project APVV-18-0052 and VEGA 1/0006/19.
The authors wish to thank the anonymous reviewers for the comments and suggestions that did lead to a significant improvement of the original manuscript.
"A heteroscedasticity diagnostic of a regression analysis with copula dependent random variables." Braz. J. Probab. Stat. 36 (2) 408 - 419, June 2022. https://doi.org/10.1214/22-BJPS532