11 papers
Federated Nonparametric Regression with Heterogeneous Clients
Xiaoran Jiang, Anand N. Vidyashankar
Whether a federated predictor benefits an individual client depends on both population heterogeneity and estimation uncertainty. We study this question for Nadaraya--Watson regress…
Domain Adaptation with Target Information via Doubly-Anchored Distributionally Robust Optimization
David Kepplinger, Anand N. Vidyashankar
Domain Adaptation (DA) often lacks worst-case guarantees, while Distributionally Robust Optimization (DRO) based only on source data centers its ambiguity set at the source law and…
Large Deviations for Controlled Branching Processes: Random Slopes, Harmonic Moments, and Transfer Principles
Miguel González, Carmen Minuesa, Inés del Puerto +1
Let \(\{X_n:n\ge0\}\) be a controlled branching process with controls \(ϕ_n\), and let \(N_n=ϕ_n(X_n)\) be its progenitor count. We study large deviations of \(X_{n+1}/N_n\), \(N_n…
Robust Estimation of Polychoric Correlation for Complex Survey Designs Using Minimum Divergence Methods
Siqi Wei, David Kepplinger, Anand N. Vidyashankar
Standard maximum likelihood estimation of polychoric correlations is highly sensitive to contamination in survey data, including response errors, interviewer effects, and careless…
Hellinger loss function for Generative Adversarial Networks
Giovanni Saraceno, Anand N. Vidyashankar, Claudio Agostinelli
We propose Hellinger-type loss functions for training Generative Adversarial Networks (GANs), motivated by the boundedness, symmetry, and robustness properties of the Hellinger dis…
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…
Sharp Large Deviations and Gibbs Conditioning for Threshold Models in Portfolio Credit Risk
Fengnan Deng, Anand N. Vidyashankar, Jeffrey F. Collamore
We obtain sharp large deviation estimates for exceedance probabilities in dependent triangular array threshold models with a diverging number of latent factors. The prefactors quan…
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…
Grouping predictors via network-wide metrics
Brandon Woosuk Park, Anand N. Vidyashankar, Tucker S. McElroy
When multitudes of features can plausibly be associated with a response, both privacy considerations and model parsimony suggest grouping them to increase the predictive power of a…