UNIVERSITY OF ILLINOIS

DEPARTMENT OF STATISTICS

SEMINAR NOTICE


The Department of Statistics

presents

Zhengjun Zhang

Washington University at St. Louis

"Quotient Correlation: A Sample Based Alternative to Pearson's Correlation"


The quotient correlation is defined here as an alternative to Pearson's correlation that is more intuitive and flexible in cases where the tail behavior of data is important. It measures nonlinear dependence where the regular correlation coefficient is generally not applicable. One of its most useful features is a test statistic that has high power when testing nonlinear dependence in cases where the Fisher's Z-transformation test may fail to reach a right conclusion. Unlike most asymptotic test statistics, which are either normal or chi-squared, this test statistic has a limiting gamma distribution (henceforth, the gamma test statistic). More than the common usages of correlation, the quotient correlation can easily and intuitively be adjusted to values at tails. This adjustment generates two new concepts -- the tail quotient correlation and the tail independence test statistics, which are also gamma statistics. Due to the fact that there is no analogue of correlation coefficient in extreme value theory, and there does not exist an efficient tail independence test statistic, these two new concepts may open up brand new field of study. In addition, an alternative to Spearman's rank correlation: rank based quotient correlation is also defined. The advantages of using these new concepts are illustrated in simulation data, and a real data analysis of air pollutants as well.


These new measures and tests can be applied to many research and practical areas, especially where the existing measures are not very suitable. If time allows, I will show statistical evidences of the existence of extreme co-movements and extreme impacts in the stock market. Some findings are not consistent with the hypothesis, theory used in the past in the financial world.

THURSDAY, September 2nd , 2004

4:00 P.M.

Room 2  Illini Hall