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• Estimating means of bounded random variables by betting AI翻译
  作者：Owen, Art B.
  作者单位：Stanford 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...
• Mode-wise principal subspace pursuit and matrix spiked covariance model AI翻译
  作者：Tang, Runshi; Yuan, Ming; Zhang, Anru R.
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Columbia University; Duke University
  摘要：This paper introduces a novel framework called Mode-wise Principal Subspace Pursuit (MOP-UP) to extract hidden variations in both the row and column dimensions for matrix data. To enhance the understanding of the framework, we introduce a class of matrix-variate spiked covariance models that serve as inspiration for the development of the MOP-UP algorithm. The MOP-UP algorithm consists of two steps: Average Subspace Capture (ASC) and Alternating Projection. These steps are specifically designe...
• Steven R Howard's contribution to the Discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas AI翻译
  作者：Howard, Steven R.
• Philip S Thomas, Erik Learned-Miller and My Phan's contribution to the Discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas AI翻译
  作者：Thomas, Philip S.; Learned-Miller, Erik; Phan, My
  作者单位：University of Massachusetts System; University of Massachusetts Amherst
• Debiased inference for a covariate-adjusted regression function AI翻译
  作者：Takatsu, Kenta; Westling, Ted
  作者单位：Carnegie Mellon University; University of Massachusetts System; University of Massachusetts Amherst; Carnegie Mellon University
  摘要：In this article, we study nonparametric inference for a covariate-adjusted regression function. This parameter captures the average association between a continuous exposure and an outcome after adjusting for other covariates. Under certain causal conditions, it also corresponds to the average outcome had all units been assigned to a specific exposure level, known as the causal dose-response curve. We propose a debiased local linear estimator of the covariate-adjusted regression function and d...
• Proposer of the vote of thanks to Waudy-Smith and Ramdas and contribution to the Discussion of 'Estimating means of bounded random variables by betting' AI翻译
  作者：Grunwald, Peter
  作者单位：Leiden University; Leiden University - Excl LUMC
• Extended fiducial inference: toward an automated process of statistical inference AI翻译
  作者：Liang, Faming; Kim, Sehwan; Sun, Yan
  作者单位：Purdue University System; Purdue University; Harvard University; Harvard Medical School; University of Pennsylvania
  摘要：While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set-'inferring the uncertainty of model parameters on the basis of observations'-has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable for big data. Extended Fiduci...
• Estimating means of bounded random variables by betting AI翻译
  作者：Neu, Gergely
  作者单位：Pompeu Fabra 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 bett...