5 papers
Adaptive Off-Policy Inference for M-Estimators Under Model Misspecification
James Leiner, Robin Dunn, Aaditya Ramdas
When data are collected adaptively, such as in bandit algorithms, classical statistical approaches such as ordinary least squares and -estimation will often fail to achieve asym…
Universal Inference Meets Random Projections: A Scalable Test for Log-concavity
Robin Dunn, Aditya Gangrade, Larry Wasserman +1
Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concav…
Gaussian Universal Likelihood Ratio Testing
Robin Dunn, Aaditya Ramdas, Sivaraman Balakrishnan +1
The classical likelihood ratio test (LRT) based on the asymptotic chi-squared distribution of the log likelihood is one of the fundamental tools of statistical inference. A recent…
A Flexible Pipeline for Prediction of Tropical Cyclone Paths
Niccolò Dalmasso, Robin Dunn, Benjamin LeRoy +1
Hurricanes and, more generally, tropical cyclones (TCs) are rare, complex natural phenomena of both scientific and public interest. The importance of understanding TCs in a changin…
Distribution-Free Prediction Sets for Two-Layer Hierarchical Models
Robin Dunn, Larry Wasserman, Aaditya Ramdas
We consider the problem of constructing distribution-free prediction sets for data from two-layer hierarchical distributions. For iid data, prediction sets can be constructed using…