9 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…
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 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…
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…