Random Threshold Driven Tail Dependence Measures with Application to Precipitation Data Analysis

摘 要：
This paper first studies the theoretical properties of the tail quotient correlation coefficient (TQCC) which was proposed to measure tail dependence between two random variables.  By introducing random thresholds in TQCC, an approximation theory between conditional tail probabilities is established. The new random threshold driven TQCC can be used to test the null hypothesis of tail independence under which TQCC test statistics are shown to follow a Chi-squared distribution under two general scenarios respectively. The TQCC is shown to be consistent under the alternative hypothesis of tail dependence with a general approximation of max-stable distribution. Second, we apply TQCC to investigate tail dependencies of a large scale applied problem of daily precipitation in continental US. Our results, in the perspective of tail dependence, reveal non-stationarity, spatial clusters, and tail dependence in the precipitations cross continental US. (This presentation is based on a joint work with Chunming Zhang and Qiurong Cui)
个人简介：
https://www.stat.wisc.edu/~zjz/index.html