Generalized Wald-type tests based on minimum density power divergence estimators

By Ayanendranath Basu, Abhijit Mandal, Nirian Martín, Leandro Pardo. 2014

48 citations recorded by the source corpus.

In testing of hypothesis, the robustness of the tests is an important concern. Generally, the maximum likelihood-based tests are most efficient under standard regularity conditions, but they are highly non-robust even under small deviations from the assumed conditions. In this paper, we have proposed generalized Wald-type tests based on minimum density power divergence estimators for parametric hypotheses. This method avoids the use of nonparametric density estimation and the bandwidth selection. The trade-off between efficiency and robustness is controlled by a tuning parameter β. The asymptotic distributions of the test statistics are chi-square with appropriate degrees of freedom. The performance of the proposed tests is explored through simulations and real data analysis.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/65cc6579f3b9f6916037c67de13091e64599b9ed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Ayanendranath Basu
2. Abhijit Mandal
3. Leandro Pardo
4. Nirian Martín
5. Abhik Ghosh

Citations in the corpus

• A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator (2016, methodology): “In this paper we shall use the density power divergence measure defined by Basu et al. (1998) because the minimum density power divergence estimators have excellent robustness properties, see for instance Basu et al. (2011; 2013; 2015; 2016), Ghosh et al. (2015; 2016b).”