2024

最新文章

• Catch me if you can: signal localization with knockoff e-values AI翻译
  作者：Gablenz, Paula; Sabatti, Chiara
  作者单位：Stanford University; Stanford University
  摘要：We consider problems where many, somewhat redundant, hypotheses are tested and we are interested in reporting the most precise rejections, with false discovery rate (FDR) control. This is the case, for example, when researchers are interested both in individual hypotheses as well as group hypotheses corresponding to intersections of sets of the original hypotheses, at several resolution levels. A concrete application is in genome-wide association studies, where, depending on the signal strengt...
• Root and community inference on the latent of a network AI翻译
  作者：Banerjee, Sayan
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erdos-Renyi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdos- Renyi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component can be regarded as noise. Giv...
• Multivariate, heteroscedastic empirical Bayes via nonparametric maximum likelihood AI翻译
  作者：Soloff, Jake A.; Guntuboyina, Adityanand; Sen, Bodhisattva
  作者单位：University of Chicago; University of California System; University of California Berkeley; Columbia University
  摘要：Multivariate, heteroscedastic errors complicate statistical inference in many large-scale denoizing problems. Empirical Bayes is attractive in such settings, but standard parametric approaches rest on assumptions about the form of the prior distribution which can be hard to justify and which introduce unnecessary tuning parameters. We extend the nonparametric maximum-likelihood estimator (NPMLE) for Gaussian location mixture densities to allow for multivariate, heteroscedastic errors. NPMLEs e...
• Yudong Chen and Yining Chen's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Chen, Yudong; Chen, Yining
  作者单位：University of London; London School Economics & Political Science
• James Jackson's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Jackson, James
  作者单位：Alan Turing Institute
  摘要：Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated b...
• Rajendra Bhansali's contribution to the Discussion of 'Parameterizing and simulating from causal models' by Evans and Didelez AI翻译
  作者：Bhansali, Rajendra
  作者单位：University of Liverpool
  摘要：Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a ...
• David Siegmund's contribution to the Discussion of Estimating means of bounded random variables by betting by Waudby-Smith and Ramdas AI翻译
  作者：Siegmund, David
  作者单位：Stanford University; 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...
• Vladimir Vovk's contribution to the Discussion of Estimating means of bounded random variables by betting by Waudby-Smith and Ramdas AI翻译
  作者：Vovk, Vladimir
  作者单位：University of London; Royal Holloway University London
• Estimating means of bounded random variables by betting AI翻译
  作者：Waudby-Smith, Ian; Ramdas, Aaditya
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon 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...
• Marco Cattaneo's contribution to the Discussion of Safe testing by Grünwald, de Heide, and Koolen AI翻译
  作者：Cattaneo, Marco E. G., V
  作者单位：University of Basel