Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 351 - Contributed Poster Presentations: Statistical Society of Canada
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #322771
Title: Asymmetric Copula Based Prediction Models
Author(s): Othmane Kortbi*
Companies: UAE University
Keywords: Asymmetric dependence; Copula; Skew-Normal copulas; Correlation; Regression ; Prediction

An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed. The method consists of introducing Skew-Normal copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An adapted algorithm is applied to construct the Skew-Normal copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program