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We present a distribution optimization framework that significantly improves confidence bounds for various risk measures compared to previous methods. Our framework encompasses popular risk measures such as the entropic risk measure,…

Machine Learning · Computer Science 2023-06-13 Hao Liang , Zhi-quan Luo

We develop new methods to integrate experimental and observational data in causal inference. While randomized controlled trials offer strong internal validity, they are often costly and therefore limited in sample size. Observational data,…

Econometrics · Economics 2025-11-04 Xuelin Yang , Licong Lin , Susan Athey , Michael I. Jordan , Guido W. Imbens

We propose a methodology for modeling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalized random measures, we consider a prior distribution for a collection of discrete random…

Methodology · Statistics 2022-06-01 Mario Beraha , Jim E. Griffin

Missing data theory deals with the statistical methods in the occurrence of missing data. Missing data occurs when some values are not stored or observed for variables of interest. However, most of the statistical theory assumes that data…

Propensity score methods are widely used for estimating treatment effects from observational studies. A popular approach is to estimate propensity scores by maximum likelihood based on logistic regression, and then apply inverse probability…

Methodology · Statistics 2017-10-24 Zhiqiang Tan

Causal inference with time-to-event outcomes is fundamental in various scientific studies. In a static setup with fitted propensity scores, weighted Kaplan-Meier estimation for survival probabilities and weighted Breslow-Peto estimation for…

Methodology · Statistics 2026-05-18 Wenfu Xu , Yi Zhang , Tobias Gerhard , Zhiqiang Tan

This paper studies large sample properties of a Bayesian approach to inference about slope parameters $\gamma$ in linear regression models with a structural break. In contrast to the conventional approach to inference about $\gamma$ that…

Econometrics · Economics 2023-08-15 Kenichi Shimizu

Optimization is widely used in statistics, and often efficiently delivers point estimates on useful spaces involving structural constraints or combinatorial structure. To quantify uncertainty, Gibbs posterior exponentiates the negative loss…

Methodology · Statistics 2025-07-23 Cheng Zeng , Eleni Dilma , Jason Xu , Leo L Duan

Bayesian inference with empirical likelihood faces a challenge as the posterior domain is a proper subset of the original parameter space due to the convex hull constraint. We propose a regularized exponentially tilted empirical likelihood…

Methodology · Statistics 2026-04-23 Eunseop Kim , Steven N. MacEachern , Mario Peruggia

Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions. In this paper, we propose a simulation-based "repro…

Statistics Theory · Mathematics 2024-11-07 Jordan Awan , Zhanyu Wang

Inspired by the large success of Transformers in Large Language Models, these architectures are increasingly applied to tabular data. This is achieved by embedding tabular data into low-dimensional Euclidean spaces resulting in similar…

Machine Learning · Computer Science 2024-09-26 Ronald Richman , Salvatore Scognamiglio , Mario V. Wüthrich

Analysis of credibility is a reverse-Bayes technique that has been proposed by Matthews (2001) to overcome some of the shortcomings of significance tests. A significant result is deemed credible if current knowledge about the effect size is…

Methodology · Statistics 2017-12-11 Leonhard Held

When prior information is lacking, the go-to strategy for probabilistic inference is to combine a "default prior" and the likelihood via Bayes's theorem. Objective Bayes, (generalized) fiducial inference, etc. fall under this umbrella. This…

Methodology · Statistics 2026-01-05 Ryan Martin

Profile likelihood confidence intervals are a robust alternative to Wald's method if the asymptotic properties of the maximum likelihood estimator are not met. However, the constrained optimization problem defining profile likelihood…

Computation · Statistics 2021-05-10 Samuel M. Fischer , Mark A. Lewis

An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through…

Methodology · Statistics 2016-09-19 Gero Walter , Louis J. M. Aslett , Frank P. A. Coolen

It is not unusual for a data analyst to encounter data sets distributed across several computers. This can happen for reasons such as privacy concerns, efficiency of likelihood evaluations, or just the sheer size of the whole data set. This…

Computation · Statistics 2018-05-22 Randy C. S. Lai , J. Hannig , Thomas C. M. Lee

Data analysis based on information from several sources is common in economic and biomedical studies. This setting is often referred to as the data fusion problem, which differs from traditional missing data problems since no complete data…

Methodology · Statistics 2022-04-07 Wei Li , Shanshan Luo , Wangli Xu

We present a multi-fidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution. In the Bayesian inference setting, traditional…

Machine Learning · Computer Science 2025-04-03 Caroline Tatsuoka , Minglei Yang , Dongbin Xiu , Guannan Zhang

The declining response rates in probability surveys along with the widespread availability of unstructured data has led to growing research into non-probability samples. Existing robust approaches are not well-developed for non-Gaussian…

Methodology · Statistics 2022-03-29 Ali Rafei , Michael R. Elliott , Carol A. C. Flannagan

We propose a robust multi-fidelity Gaussian process for integrating sparse, high-quality reference monitors with dense but noisy citizen-science sensors. The approach replaces the Gaussian log-likelihood in the high-fidelity channel with a…

Methodology · Statistics 2025-11-21 Camilla Andreozzi , Pietro Colombo , Philipp Otto