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作者：Kuang, Qi; Wang, Chao; Jiao, Yuling; Zhou, Fan
作者单位：Jiangxi University of Finance & Economics; Jiangxi University of Finance & Economics; Shanghai University of Finance & Economics; Wuhan University; Wuhan University
摘要：This article investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presenting a novel algorithm called Deep Quantile Process regression-based Off-Policy Evaluation (DQPOPE). We provide new theoretical insights in...
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作者：Franzolini, Beatrice; De Iorio, Maria; Eriksson, Johan
作者单位：Bocconi University; National University of Singapore; Agency for Science Technology & Research (A*STAR)
摘要：Standard clustering techniques assume a common clustering configuration for all features in a dataset. However, when dealing with multi-view or longitudinal data, the clusters' number, frequencies, and shapes may need to vary across features to accurately capture dependence structures and heterogeneity. In this setting, classical model-based clustering fails to account for within-subject dependence across domains. We introduce conditional partial exchangeability, a novel probabilistic paradigm...
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作者：Ki, Dohyeong; Guntuboyina, Adityanand
作者单位：University of California System; University of California Berkeley
摘要：Shape constraints in nonparametric regression provide a powerful framework for estimating regression functions under realistic assumptions without tuning parameters. However, most existing methods-except additive models-impose too weak restrictions, often leading to overfitting in high dimensions. Conversely, additive models can be too rigid, failing to capture covariate interactions. This article introduces a novel multivariate shape-constrained regression approach based on total concavity, o...
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作者：Wang, Jiayi; Shi, Chengchun; Qi, Zhengling
作者单位：University of Texas System; University of Texas Dallas; University of London; London School Economics & Political Science; George Washington University
摘要：As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy learning for the decision ...
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作者：Lin, Xihong
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
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作者：Garcia-Portugues, Eduardo; Paindaveine, Davy; Verdebout, Thomas
作者单位：Universidad Carlos III de Madrid; Universite Libre de Bruxelles; Universite Libre de Bruxelles
摘要：We consider a broad class of symmetry hypothesis testing problems that includes the problems of testing uniformity or rotational symmetry on the hypersphere Sd-1, as well as the problem of testing sphericity in R-d. For this class, we study the null and non-null behaviors of Sobolev tests, with emphasis on their consistency rates and corresponding asymptotic powers. Our main results show that: (i) Sobolev tests exhibit a detection threshold that depends not only on the coefficients defining th...
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作者：Halder, Aritra; Li, Didong; Banerjee, Sudipto
作者单位：Drexel University; University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Los Angeles
摘要：Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the values of the random field (or spatial surface indexed continuously over time) at arbitrary space-time coordinates, scientific interest often turns to gleaning information regarding zones of rapid spatial-temporal change. We develop Bayesian modeling and inference for directional rates of change along a given surface...
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作者：Zhang, Yi; Xu, Wenfu; Tan, Zhiqiang
作者单位：Rutgers University System; Rutgers University New Brunswick; China Jiliang University
摘要：Sensitivity analysis is important to assess the impact of unmeasured confounding in causal inference from observational studies. The marginal sensitivity model (MSM) provides a useful approach in quantifying the influence of unmeasured confounders on treatment assignment and leading to tractable sharp bounds of common causal parameters. In this article, to tighten MSM sharp bounds, we propose the enhanced MSM (eMSM) by incorporating another sensitivity constraint which quantifies the influence...
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作者：Martin, Ryan
作者单位：North Carolina State University
摘要：Inferential models (IMs) offer prior-free, Bayesian-like posterior degrees of belief designed for statistical inference, which feature a frequentist-like calibration property that ensures reliability of said inferences. The catch is that IMs' degrees of belief are possibilistic rather than probabilistic and, since the familiar Monte Carlo methods approximate probabilistic quantities, there are significant computational challenges associated with putting this framework into practice. The presen...
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作者：Li, Jiawei; Wang, Jingshen
作者单位：Boston University; Boston University
摘要：Assessing how well a Bayesian model generalizes to unobserved data is essential, yet existing general-purpose model checks are either not properly calibrated (as in posterior predictive checks) or fail to be sufficiently general for practical use (e.g., due to requiring model-specific derivations). We propose split predictive checks (SPCs) as a simple, general-purpose class of predictive checks that maintain the usability of posterior predictive checks while directly targeting predictive gener...
