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20172020
most citedRobust Hypothesis Testing and Model Selection for Parametric Proportional Hazard Regression Models

1 citations · 1 across the 3 of their papers we have counts for

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7 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…

math.ST2019

Density Power Downweighting and Robust Inference: Some New Strategies

Saptarshi Roy, Kaustav Chakraborty, Somnath Bhadra +1

Preserving the robustness of the procedure has, at the present time, become almost a default requirement for statistical data analysis. Since efficiency at the model and robustness…

stat.AP2019

Robust Inference for Skewed data in Health Sciences

Amarnath Nandy, Ayanendranath Basu, Abhik Ghosh

Health data are often not symmetric to be adequately modeled through the usual normal distributions; most of them exhibit skewed patterns. They can indeed be modeled better through…

stat.ME2019

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…