Abstract Details
Activity Number:
|
657
|
Type:
|
Invited
|
Date/Time:
|
Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
IMS
|
Abstract - #307065 |
Title:
|
Goodness-of-Fit Tests for Long Memory Moving-Average Marginal Density
|
Author(s):
|
Hira Lal Koul*+ and Nao Mimoto and Donatas Surgailis
|
Companies:
|
Michigan State University and Michigan State University and Vilnius University
|
Keywords:
|
Kernel density estimator ;
chi square distribution
|
Abstract:
|
In this talk we will discuss the problem of fitting a known d.f. or density to the marginal error density of a stationary long memory moving-average process when its mean is known and unknown. When the mean is unknown and estimated by the sample mean, the first-order difference between the residual empirical and null distribution functions is known to be asymptotically degenerate at zero. Hence, it cannot be used to fit a distribution up to an unknown mean. However, we shall show that by using a suitable class of estimators of the mean, this first order degeneracy does not occur. We also present some large sample properties of the tests based on an integrated squared-difference between kernel-type error density estimators and the expected value of the error density estimator based on errors. The asymptotic null distributions of suitably standardized test statistics are shown to be chi-square with one degree of freedom in both cases of known and unknown mean. This is totally unlike the i.i.d. errors set-up where suitable standardizations of these statistics are known to be asymptotically normally distributed.
|
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.