讲座时间：2014.1.2下午13:30-15:00
讲座人：Zhengjun Zhang, University of Wisconsin讲座摘要：
Applicability of Pearson's correlation as a measure of explained variance is by now well understood. One of its limitations is that it does not account for asymmetry in explained variance and tail dependence. In the era of Big Data, new statistical analytical tools are in demand.
Aiming to obtain broad applicable correlation measures, we first use a pair of r-squares of generalized regression to deal with asymmetries in explained variances, and linear or nonlinear relations between random variables.We call the pair of r-squares of generalized measures of correlation (GMC). We present examples under which the paired measures are identical, and they become a symmetric correlation measure which is the same as the squared Pearson's correlation coefficient. As a result, Pearson's correlation is a special case of GMC. On the other hand, identification of tail dependence among observations is important and challenging, but remains an open problem, due to the fact that tail dependence is primarily captured by values above thresholds. This talk introduces a class of tail quotient correlation coefficients (TQCC ）which allows the underlying threshold values to be random. TQCC is practically appealing.
Both GMC and TQCC can be applicable in numerous applications and can lead to more meaningful conclusions and decision making. This talk will mainly illustrate applications in financial markets. Other potential applications will be discussed as well.
讲座时间：2014.1.2下午15:00-16:30
