56 citations · 221 across the 15 of their papers we have counts for
16 papers
Robust K-means Clustering using the Density Power Divergence Measure
Anirban Mondal, Paromita Banerjee, Abhijit Mandal
We introduce a robust clustering method, MK-means DPD, that estimates cluster centers and covariance matrices using density power divergence (DPD) measures combined with Mahalanobi…
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…
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…
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…
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 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…
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…
Stratified Random Sampling for Dependent Inputs
Anirban Mondal, Abhijit Mandal
A new approach of obtaining stratified random samples from statistically dependent random variables is described. The proposed method can be used to obtain samples from the input s…
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…
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…
Robust Wald-type test in GLM with random design based on minimum density power divergence estimators
Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal +2
We consider the problem of robust inference under the generalized linear model (GLM) with stochastic covariates. We derive the properties of the minimum density power divergence es…
A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator
Ayandrendanath Basu, Abhik Ghosh, Abhijit Mandal +2
In this paper a robust version of the classical Wald test statistics for linear hypothesis in the logistic regression model is introduced and its properties are explored. We study…
Influence Analysis of Robust Wald-type Tests
Abhik Ghosh, Abhijit Mandal, Nirian Martin +1
We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based o…
Generalized Wald-type Tests based on Minimum Density Power Divergence Estimators
Ayanendranath Basu, Abhijit Mandal, Nirian Martin +1
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,…
Robust Tests for the Equality of Two Normal Means based on the Density Power Divergence
Ayanendranath Basu, Abhijit Mandal, Nirian Martin +1
Statistical techniques are used in all branches of science to determine the feasibility of quantitative hypotheses. One of the most basic applications of statistical techniques in…
Testing Composite Hypothesis based on the Density Power Divergence
Ayanendranath Basu, Abhijit Mandal, Nirian Martin +1
In any parametric inference problem, the robustness of the procedure is a real concern. A procedure which retains a high degree of efficiency under the model and simultaneously pro…