68 citations · 377 across the 50 of their papers we have counts for
57 papers
A Composite Divergence Approach to Robust Multivariate Estimation under Cellwise and Casewise Contamination
Abhik Ghosh, Claudio Agostinelli, Ayanendranath Basu
Composite likelihood (CL) methods provide a computationally efficient alternative to full likelihood inference for complex multivariate models by replacing the joint likelihood wit…
Universally Optimal Robustness-Efficiency Tradeoffs for a General Class of Minimum Divergence Estimators
Subhrajyoty Roy, Supratik Basu, Abhik Ghosh +1
Balancing the efficiency of an estimator under ideal conditions against its robustness under contamination remains a central challenge in robust statistics. While minimum divergenc…
Semiparametric Robust Estimation of Population Location
Ananyabrata Barua, Ayanendranath Basu
Real-world measurements often comprise a dominant signal contaminated by a noisy background. Robustly estimating the dominant signal in practice has been a fundamental statistical…
Robust Rank Estimation for Noisy Matrices
Subhrajyoty Roy, Abhik Ghosh, Ayanendranath Basu
Estimating the true rank of a noisy data matrix is a fundamental problem underlying techniques such as principal component analysis, matrix completion, etc. Existing rank estimatio…
Asymptotic breakdown point analysis of the minimum density power divergence estimator under independent non-homogeneous setups
Suryasis Jana, Subhrajyoty Roy, Ayanendranath Basu +1
The minimum density power divergence estimator (MDPDE) has gained significant attention in the literature of robust inference due to its strong robustness properties and high asymp…
Characterization of Generalized Alpha-Beta Divergence and Associated Entropy Measures
Subhrajyoty Roy, Supratik Basu, Abhik Ghosh +1
Minimum divergence estimators provide a natural framework for robust (parametric) statistical inference. Useful properties of several such divergence measures, including, the Helli…
A Componentwise Estimation Procedure for Multivariate Location and Scatter: Robustness, Efficiency and Scalability
Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh
Covariance matrix estimation is an important problem in multivariate data analysis, both from theoretical as well as applied points of view. Many simple and popular covariance matr…
Robust inference for linear regression models with possibly skewed error distribution
Amarnath Nandy, Ayanendranath Basu, Abhik Ghosh
Traditional methods for linear regression generally assume that the underlying error distribution, equivalently the distribution of the responses, is normal. Yet, sometimes real li…
Robust Principal Component Analysis using Density Power Divergence
Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh
Principal component analysis (PCA) is a widely employed statistical tool used primarily for dimensionality reduction. However, it is known to be adversely affected by the presence…
Analysis of the rSVDdpd Algorithm: A Robust Singular Value Decomposition Method using Density Power Divergence
Subhrajyoty Roy, Abhik Ghosh, Ayanendranath Basu
The traditional method of computing singular value decomposition (SVD) of a data matrix is based on a least squares principle, thus, is very sensitive to the presence of outliers.…
Asymptotic Breakdown Point Analysis for a General Class of Minimum Divergence Estimators
Subhrajyoty Roy, Abir Sarkar, Abhik Ghosh +1
Robust inference based on the minimization of statistical divergences has proved to be a useful alternative to classical techniques based on maximum likelihood and related methods.…
Robust and Efficient Estimation in Ordinal Response Models using the Density Power Divergence
Arijit Pyne, Subhrajyoty Roy, Abhik Ghosh +1
In real life, we frequently come across data sets that involve some independent explanatory variable(s) generating a set of ordinal responses. These ordinal responses may correspon…
Existence and Consistency of the Maximum Pseudo \b{eta}-Likelihood Estimators for Multivariate Normal Mixture Models
Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh
Robust estimation under multivariate normal (MVN) mixture model is always a computational challenge. A recently proposed maximum pseudo \b{eta}-likelihood estimator aims to estimat…
rSVDdpd: A Robust Scalable Video Surveillance Background Modelling Algorithm
Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh
A basic algorithmic task in automated video surveillance is to separate background and foreground objects. Camera tampering, noisy videos, low frame rate, etc., pose difficulties i…
Robust adaptive Lasso in high-dimensional logistic regression
Ayanendranath Basu, Abhik Ghosh, María Jaenada +1
Penalized logistic regression is extremely useful for binary classification with large number of covariates (higher than the sample size), having several real life applications, in…
Characterizing the Functional Density Power Divergence Class
Souvik Ray, Subrata Pal, Sumit Kumar Kar +1
Divergence measures have a long association with statistical inference, machine learning and information theory. The density power divergence and related measures have produced man…
Characterizing Logarithmic Bregman Functions
Souvik Ray, Subrata Pal, Sumit Kumar Kar +1
Minimum divergence procedures based on the density power divergence and the logarithmic density power divergence have been extremely popular and successful in generating inference…
The extended Bregman divergence and parametric estimation
Sancharee Basak, Ayanendranath Basu
Minimization of suitable statistical distances~(between the data and model densities) has proved to be a very useful technique in the field of robust inference. Apart from the clas…
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 Estimation under Linear Mixed Models: The Minimum Density Power Divergence Approach
Giovanni Saraceno, Abhik Ghosh, Ayanendranath Basu +1
Many real-life data sets can be analyzed using Linear Mixed Models (LMMs). Since these are ordinarily based on normality assumptions, under small deviations from the model the infe…
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…
Robust Clustering with Normal Mixture Models: A Pseudo -Likelihood Approach
Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh
As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from…
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…
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…