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Activity Number: 414 - Model Building and Selection
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #322501
Title: Copula Modeling for Data with Ties
Author(s): Yichen Qin* and Yan Li and Yang Li and Jun Yan
Companies: University of Cincinnati and University of Connecticut and Renmin University of China and University of Connecticut
Keywords: interval censoring ; multivariate distribution ; pseudo-observations ; rank
Abstract:

Copula modeling has gained much attention in many fields recently with the advantage of separating dependence structure from marginal distributions. In real data, however, serious ties are often present in one or multiple margins, which cause problems to many rank-based statistical methods developed under the assumption of continuous data with no ties. Simple methods such as breaking the ties at random or using average rank introduce independence into the data and, hence, lead to biased estimation. We propose an estimation method that treats the ranks of tied data as being interval censored and maximizes a pseudo-likelihood based on interval censored pseudo-observations. A parametric bootstrap procedure that preserves the observed tied ranks in the data is adapted to assess the estimation uncertainty and perform goodness-of-fit tests. The proposed approach is shown to be very competitive in comparison to the simple treatments in a large scale simulation study. Application to a bivariate insurance data illustrates the methodology.


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

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