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20142020
most citedThe Logarithmic Super Divergence and its use in Statistical Inference

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME20201 cited

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…

math.ST2020

On minimum Bregman divergence inference

Soumik Purkayastha, Ayanendranath Basu

In this paper a new family of minimum divergence estimators based on the Bregman divergence is proposed. The popular density power divergence (DPD) class of estimators is a sub-cla…

math.ST2019

On Robust Pseudo-Bayes Estimation for the Independent Non-homogeneous Set-up

Tuhin Majumder, Ayanendranath Basu, Abhik Ghosh

The ordinary Bayes estimator based on the posterior density suffers from the potential problems of non-robustness under data contamination or outliers. In this paper, we consider t…

stat.ME20144 cited

The Logarithmic Super Divergence and its use in Statistical Inference

Avijit Maji, Abhik Ghosh, Ayanendranath Basu

This paper introduces a new superfamily of divergences that is similar in spirit to the S-divergence family introduced by Ghosh et al. (2013). This new family serves as an umbrella…

stat.ME20141 cited

Estimation of Multivariate Location and Covariance using the S -Hellinger Distance

Abhik Ghosh, Ayanendranath Basu

This paper describes a generalization of the Hellinger distance which we call the S -Hellinger distance; this general family connects the Hellinger distance smoothly with the