Testing stationary distributions of Markov chains based on Rao's divergence
β Scribed by M.C. Pardo
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 310 KB
- Volume
- 12
- Category
- Article
- ISSN
- 0893-9659
No coin nor oath required. For personal study only.
β¦ Synopsis
Statistical inference problems such as the estimation of parameters and testing composite hypothesis about stationary distributions in the set of states of Markov chains are solved. Both, the estimator and the statistic proposed are based on Rao's divergence. The asymptotic properties of the estimator and the critical values of asymptotically -/-level tests are obtained. ~
π SIMILAR VOLUMES
This paper investigates a new family of statistics based on Burbea Rao divergence for testing goodness-of-fit. Under the simple and composite null hypotheses the asymptotic distribution of these tests is shown to be chi-squared. For composite hypothesis, the unspecified parameters are estimated by m
When using a test which is reliant on asymptotic results to obtain the critical regions, the first question to answer is how the test performs in finite samples. This paper provides answers to this question in relation with the Re-divergence family of goodness-of-fit tests (Pardo, 1997). The asympto