4 papers · 1 filter
Divergence-Minimization for Latent-Structure Models: Monotone Operators, Contraction Guarantees, and Robust Inference
Lei Li, Anand N. Vidyashankar
We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted divergence, the DM approach recov…
Minimum Hellinger Distance Estimators for Complex Survey Designs
David Kepplinger, Anand N. Vidyashankar
Reliable inference from complex survey samples can be derailed by outliers and high-leverage observations induced by unequal inclusion probabilities and calibration. We develop a m…
Ancestral Inference and Learning for Branching Processes in Random Environments
Xiaoran Jiang, Anand N. Vidyashankar
Ancestral inference for branching processes in random environments involves determining the ancestor distribution parameters using the population sizes of descendant generations. I…
Private Minimum Hellinger Distance Estimation via Hellinger Distance Differential Privacy
Fengnan Deng, Anand N. Vidyashankar
Objective functions based on Hellinger distance yield robust and efficient estimators of model parameters. Motivated by privacy and regulatory requirements encountered in contempor…