A Robust Generalization of the Rao Test
Bibliographic Data
| ID | 19419707 |
|---|---|
| Authors | Ayanendranath Basu (0000-0003-1416-9109, Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata, India), Abhik Ghosh (0000-0003-3688-4584, Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata, India), Nirian Martin (0000-0003-4766-1909, Interdisciplinary Mathematics Institute, Complutense University of Madrid, Madrid, Spain, corresponding author), Leandro Pardo (0000-0003-2005-8245, Interdisciplinary Mathematics Institute, Complutense University of Madrid, Madrid, Spain) |
| Year | 2022 |
| Volume | 40 |
| Issue | 2 |
| Pages | 868-879 |
| Publication date | 2022-04-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2021.1876711 |
| OpenAlex | W2969736462 |
| Language | EN |
| References cited | 25 |
This article presents new families of Rao-type test statistics based on the minimum density power divergence estimators which provide robust generalizations for testing simple and composite null hypotheses. The asymptotic null distributions of the proposed tests are obtained and their robustness properties are also theoretically studied. Numerical illustrations are provided to substantiate the theory developed. On the whole, the proposed tests are seen to be excellent alternatives to the classical Rao test as well as other well-known tests
Algorithm · Alternative hypothesis · Data mining · Estimator · Generalization · Mathematical analysis · Mathematical optimization · Null hypothesis · Statistical hypothesis testing · Statistics · Advanced Statistical Methods and Models · Computer Science · Mathematics · Statistical Distribution Estimation and Applications · Statistical Methods and Inference · Applied Mathematics
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| Highly cited | No |
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