10 papers
Direct and efficient estimation of bilinear forms in staggered tensor panels
Alberto Bordino, Thomas B. Berrett, Olga Klopp
We study the estimation of bilinear forms from noisy, partially observed tensor data. The signal follows a Tucker2 model, with shared unit and time factors across tensor layers and…
Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contamination
Mengqi Chen, Thomas B. Berrett, Theodoros Damoulas +1
Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set that can capture distributional shifts in out-of-sample environments. While…
Estimation beyond Missing (Completely) at Random
Tianyi Ma, Kabir A. Verchand, Thomas B. Berrett +2
We study the effects of missingness on the estimation of population parameters. Moving beyond restrictive missing completely at random (MCAR) assumptions, we first formulate a miss…
Total robustness in Bayesian Nonlinear Regression
Mengqi Chen, Charita Dellaporta, Thomas B. Berrett +1
Modern regression analyses are often undermined by covariate measurement error, misspecification of the regression model, and misspecification of the measurement error distribution…
Locally Differentially Private Two-Sample Testing
Alexander Kent, Thomas B. Berrett, Yi Yu
We consider the problem of two-sample testing under a local differential privacy constraint where a permutation procedure is used to calibrate the tests. We develop testing procedu…
Semi-supervised linear regression with missing covariates
Benedict M. Risebrow, Thomas B. Berrett
Missing values in datasets are common in applied statistics. For regression problems, theoretical work thus far has largely considered the issue of missing covariates as distinct f…