JSM 2005 Online Program

Abstract #304275

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	69
Type:	Contributed
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Nonparametric Statistics
Abstract - #304275
Title:	The Effect of Serial Correlation on the Shapiro-Wilk and Wilcoxon Tests
Author(s):	Yulia Gel*+ and Weiwen Miao and Joseph L. Gastwirth
Companies:	University of Waterloo and Macalester College and George Washington University
Address:	200 University Ave. W., Waterloo, ON, N2L 3G1, Canada
Keywords:	nonparametric tests ; serial correlation ; modelling ; randomness ; time series analysis
Abstract:	When data are obtained in a time sequence, the observations usually turn out to be serially correlated. In most business and economic time series, the serial correlation is positive, (i.e., if the observation is positive [negative] on a given time period, it often is likely with such data that the observation for the following time period also is positive [negative]). It is known that the serial correlation adversely affects many nonparametric tests such as the Shapiro-Wilk test, the Wilcoxon test, and the Pearson and Spearman correlation. The p-values obtained using these nonparametric tests are shown to be inflated or deflated depending on correlation structure of the observations. We will present a study of the dependence effect on the Shapiro-Wilk and Wilcoxon tests for normal and exponential distribution. The implications of our results on the statistical analysis of data arising in a securities law case will be discussed.

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