Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1150420 | Journal of Statistical Planning and Inference | 2009 | 8 Pages |
Abstract
We propose a new nonparametric test to test for symmetry and separability of space–time covariance functions. Unlike the existing nonparametric tests, our test has the attractive convenience of being free of choosing any user-chosen number or smoothing parameter. The asymptotic null distributions of the test statistics are free of nuisance parameters and the critical values have been tabulated in the literature. From a practical point of view, our test is easy to implement and can be readily used by the practitioner. A Monte-Carlo experiment and real data analysis illustrate the finite sample performance of the new test.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Xiaofeng Shao, Bo Li,