8 papers
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…
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…
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 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…