Name
Abstract | ||
|---|---|---|
In this article, we propose a test for homogeneity based on Kullback-Leibler information (also known as relative entropy). Though widely used in hypothesis testing problems, Kullback-Leibler information is not desirable to many researchers in the context of mixture because of its complicated form. In this article, a weighted relative entropy test (WE test), which has closed form expression in terms of the parameter estimators, is proposed. Theoretical results show that this test is consistent. Some simulation results demonstrate that the WE test is better than some leading tests when the mixture components come from normal distribution, and is competitive with them in the Poisson case. The usage of the test is illustrated in an example with data about acidity index of lakes. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1080/03610910802305009 | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
Keywords | DocType | Volume |
Kullback-Leibler (K-L) information, Maximum-likelihood estimate, Mixture, Test for homogeneity, WE (weighted relative entropy) test | Journal | 37 |
Issue | ISSN | Citations |
10 | 0361-0918 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulin Li | 1 | 15 | 3.28 |
| Liuxia Wang | 2 | 0 | 1.01 |