Abstract Details
Activity Number:
|
82
|
Type:
|
Contributed
|
Date/Time:
|
Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract - #307602 |
Title:
|
New Invariant and Consistent Chi-squared Type Goodness-of-fit Tests for Multivariate Normality and a Related Comparative Simulation Study
|
Author(s):
|
Vassiliy Voinov*+ and Natalie Pya and Rashid Makarov and Yevgeniy Voinov
|
Companies:
|
KIMEP University and KIMEP University and KIMEP University and KIMEP University
|
Keywords:
|
Chi-squared goodness-of-fit tests ;
invariant and consistent tests ;
multivariate normality ;
symmetric alternatives ;
power of tests
|
Abstract:
|
New chi-squared type invariant and consistent goodness-of-fit tests for multivariate normality are introduced. The tests are based on the Karhunen-Loève transformation of a multi-dimensional sample from a population. This transformation diagonalizes the sample covariance matrix. Then a modification of Moore and Stubblebine technique for construction Wald's type chi-squared tests was used. A comparison of simulated powers of these tests and some other well known tests with respect to seven different symmetrical multivariate alternatives is given. The simulation study demonstrates that the power of the proposed McCull test almost does not depend on the number of grouping cells. The test shows an advantage over other chi-squared type tests and is more powerful than several known tests against some alternatives.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.