B1187
Title: Kendall's tau estimator for zero-inflated discrete distributions
Authors: Elisa Perrone - Eindhoven University of Technology (Netherlands) [presenting]
Zhuozhao Zhan - Eindhoven University of Technology (Netherlands)
Edwin van den Heuvel - Eindhoven University of Technology (Netherlands)
Abstract: Zero-inflated data naturally appears in many applications such as health care, weather forecasting, and insurance. Analyzing zero-inflated data is challenging as the high amount of observations in zero invalidates standard statistical techniques. For example, assessing the level of dependence between two zero-inflated random variables becomes difficult due to limitations when applying standard rank-based measures of association, such as Kendall's tau or Spearman's rho. Recent work tackles this issue and suggests an estimator of Kendall's tau for zero-inflated continuous distributions. However, such an estimator does not show satisfactory performances for zero-inflated count data. We fill this gap and propose an adjusted estimator specific for zero-inflated discrete distributions. We derive the estimator analytically and show that it outperforms existing estimators in various simulated scenarios. Finally, we investigate the interpretability of the proposed estimator by studying its achievable bounds.
