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Abstract:
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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 using Lin's concordance measure.
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