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作者：Larsson, Martin; Ruf, Johannes
作者单位：Carnegie Mellon University; University of London; London School Economics & Political Science; University of London; London School Economics & Political Science
摘要：We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form empirical-Bernstein CSs and CIs (converging exactly to the oracle Bernstein width) as well as non-closed-form betting CSs and CIs. Our method combines new composite nonnegative (super) martingales with Ville's maximal inequality, with strong connections to testing by bet...
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作者：Catalano, Marta; Fasano, Augusto; Rebaudo, Giovanni
作者单位：University of Warwick; University of Turin
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作者：Zhang, Yi; Shao, Xiaofeng
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
摘要：The bandwidth-free tests for a multi-dimensional parameter have attracted considerable attention in econometrics and statistics literature. These tests can be conveniently implemented due to their tuning-parameter free nature and possess more accurate size as compared to the traditional heteroskedasticity and autocorrelation consistent-based approaches. However, when sample size is small/medium, these bandwidth-free tests exhibit large size distortion when both the dimension of the parameter a...
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作者：Greenland, Sander
作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
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作者：Jiang, Rong; Yu, Keming
作者单位：Shanghai Polytechnic University; Brunel University
摘要：We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form empirical-Bernstein CSs and CIs (converging exactly to the oracle Bernstein width) as well as non-closed-form betting CSs and CIs. Our method combines new composite nonnegative (super) martingales with Ville's maximal inequality, with strong connections to testing by bet...
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作者：Mueller, Manuel M.; Reeve, Henry W. J.; Cannings, Timothy, I; Samworth, Richard J.
作者单位：University of Cambridge; University of Bristol; University of Edinburgh; Heriot Watt University; University of Edinburgh
摘要：Given a sample of covariate-response pairs, we consider the subgroup selection problem of identifying a subset of the covariate domain where the regression function exceeds a predetermined threshold. We introduce a computationally feasible approach for subgroup selection in the context of multivariate isotonic regression based on martingale tests and multiple testing procedures for logically structured hypotheses. Our proposed procedure satisfies a non-asymptotic, uniform Type I error rate gua...
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作者：Gruenwald, Peter; de Heide, Rianne; Koolen, Wouter
作者单位：Leiden University - Excl LUMC; Leiden University; Vrije Universiteit Amsterdam; University of Twente
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作者：Wang, Ruodu
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作者：Nguyen, Hien
作者单位：University of Queensland; University of Queensland
摘要：We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form empirical-Bernstein CSs and CIs (converging exactly to the oracle Bernstein width) as well as non-closed-form betting CSs and CIs. Our method combines new composite nonnegative (super) martingales with Ville's maximal inequality, with strong connections to testing by bet...
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作者：Egami, Naoki; Tchetgen, Eric J. Tchetgen
作者单位：Columbia University; University of Pennsylvania; University of Pennsylvania
摘要：Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmea...
