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20182021
most citedRobust Variable Selection Criteria for the Penalized Regression

1 citations · 1 across the 5 of their papers we have counts for

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10 papers

stat.ME2021

Robust Density Power Divergence Estimates for Panel Data Models

Abhijit Mandal, Beste Hamiye Beyaztas, Soutir Bandyopadhyay

The panel data regression models have become one of the most widely applied statistical approaches in different fields of research, including social, behavioral, environmental scie…

stat.ME2021

A robust specification test in linear panel data models

Beste Hamiye Beyaztas, Soutir Bandyopadhyay, Abhijit Mandal

The presence of outlying observations may adversely affect statistical testing procedures that result in unstable test statistics and unreliable inferences depending on the distort…

stat.CO2021

A Two Stage Adaptive Metropolis Algorithm

Anirban Mondal, Kai Yin, Abhijit Mandal

We propose a new sampling algorithm combining two quite powerful ideas in the Markov chain Monte Carlo literature -- adaptive Metropolis sampler and two-stage Metropolis-Hastings s…

stat.ME2020

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…

stat.ME20191 cited

Robust Variable Selection Criteria for the Penalized Regression

Abhijit Mandal, Samiran Ghosh

We propose a robust variable selection procedure using a divergence based M-estimator combined with a penalty function. It produces robust estimates of the regression parameters an…

stat.ME2019

An Optimal Test for the Additive Model with Discrete or Categorical Predictors

Abhijit Mandal

In multivariate nonparametric regression the additive models are very useful when a suitable parametric model is difficult to find. The backfitting algorithm is a powerful tool to…