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Robust Inference Using the Exponential-Polynomial Divergence
Pushpinder Singh, Abhijit Mandal, Ayanendranath Basu
Density-based minimum divergence procedures represent popular techniques in parametric statistical inference. They combine strong robustness properties with high (sometimes full) a…
Robust Hypothesis Testing and Model Selection for Parametric Proportional Hazard Regression Models
Amarnath Nandy, Abhik Ghosh, Ayanendranath Basu +1
The semi-parametric Cox proportional hazards regression model has been widely used for many years in several applied sciences. However, a fully parametric proportional hazards mode…
A Robust Generalization of the Rao Test
Ayanendranath Basu, Abhik Ghosh, Nirian Martin +1
This paper presents new families of Rao-type test statistics based on the minimum density power divergence estimators which provide robust generalizations for testing simple and co…
The B-Exponential Divergence and its Generalizations with Applications to Parametric Estimation
Taranga Mukherjee, Abhijit Mandal, Ayanendranath Basu
In this paper a new family of minimum divergence estimators based on the Bregman divergence is proposed, where the defining convex function has an exponential nature. These estimat…
Robust and Efficient Estimation in the Parametric Cox Regression Model under Random Censoring
Abhik Ghosh, Ayanendranath Basu
Cox proportional hazard regression model is a popular tool to analyze the relationship between a censored lifetime variable with other relevant factors. The semi-parametric Cox mod…
A Robust Wald-type Test for Testing the Equality of Two Means from Log-Normal Samples
Ayanendranath Basu, Abhijit Mandal, Nirian Martin +1
The log-normal distribution is one of the most common distributions used for modeling skewed and positive data. It frequently arises in many disciplines of science, specially in th…