9 papers
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…
Provably robust learning of regression neural networks using -divergences
Abhik Ghosh, Suryasis Jana
Regression neural networks (NNs) are most commonly trained by minimizing the mean squared prediction error, which is highly sensitive to outliers and data contamination. Existing r…
Robust Estimation for Dependent Binary Network Data
Tianyu Liu, Somabha Mukherjee, Abhik Ghosh
We consider the problem of learning the interaction strength between the nodes of a network based on dependent binary observations residing on these nodes, generated from a Markov…
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…
On Generalized Likelihood Estimation Based on the Logarithmic Norm Relative Entropy
Himanshi Singh, Abhik Ghosh, Nil Kamal Hazra
Traditional likelihood based methods for parameter estimation get highly affected when the given data is contaminated by outliers even in a small proportion. In this paper, we cons…
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…
Robust Inference for Non-Linear Regression Models with Applications in Enzyme Kinetics
Suryasis Jana, Abhik Ghosh
Despite linear regression being the most popular statistical modelling technique, in real-life we often need to deal with situations where the true relationship between the respons…
Conditional variable screening for ultra-high dimensional longitudinal data with time interactions
Andrea Bratsberg, Abhik Ghosh, Magne Thoresen
In recent years we have been able to gather large amounts of genomic data at a fast rate, creating situations where the number of variables greatly exceeds the number of observatio…