Journal of the Royal Statistical Society: Series B

最新文章

• Testing for a change in mean after changepoint detection AI翻译
  作者：Jewell, Sean; Fearnhead, Paul; Witten, Daniela
  作者单位：University of Washington; University of Washington Seattle; Lancaster University; University of Washington; University of Washington Seattle
  摘要：While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework in the case of changepoints estimated by binary segmentation and its variants, l0 segmentation, o...
• Prediction and outlier detection in classification problems AI翻译
  作者：Guan, Leying; Tibshirani, Robert
  作者单位：Yale University; Stanford University
  摘要：We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanced and conformal optimized prediction sets). BCOPS constructs a prediction set C(x) as a subset of class labels, possibly empty. It tries to optimize the out-of-sample performance, aiming to include the correct class and to detect outliers x as often as possible. BCOPS returns no prediction (corresponding to C(x) equ...
• The sceptical Bayes factor for the assessment of replication success AI翻译
  作者：Pawel, Samuel; Held, Leonhard
  作者单位：University of Zurich; Swiss School of Public Health (SSPH+)
  摘要：Replication studies are increasingly conducted but there is no established statistical criterion for replication success. We propose a novel approach combining reverse-Bayes analysis with Bayesian hypothesis testing: a sceptical prior is determined for the effect size such that the original finding is no longer convincing in terms of a Bayes factor. This prior is then contrasted to an advocacy prior (the reference posterior of the effect size based on the original study), and replication succe...
• Priyantha Wijayatunga's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Wijayatunga, Priyantha
  作者单位：Umea University
• Empirical likelihood-based inference for functional means with application to wearable device data AI翻译
  作者：Chang, Hsin-wen; McKeague, Ian W.
  作者单位：Academia Sinica - Taiwan; Columbia University
  摘要：This paper develops a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. These curves consider all activity levels within the range of device readings, which is preferable to the practice of classifying activity into discrete categories. Motivated by certain features of these curves, we introduce a powerful likelihood ratio approach to construct confidence bands and compare functional means. Notably, our approach allows discontinui...
• Conditional independence testing in Hilbert spaces with applications to functional data analysis AI翻译
  作者：Lundborg, Anton Rask; Shah, Rajen D.; Peters, Jonas
  作者单位：University of Cambridge; University of Copenhagen
  摘要：We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalises testing the significance of X in a regression model of scalar response Y on functional regressors X and Z. We show, however, that even in the idealised setting where additionally (X, Y, Z) has a Gaussian distribution, the power of any test cannot exceed its size. Further modelling assumptions are needed and we argu...
• Pallavi Basu's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Basu, Pallavi
  作者单位：Indian School of Business (ISB)
• Seconder of the vote of thanks to Vansteelandt and Dukes and contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' AI翻译
  作者：Didelez, Vanessa
  作者单位：Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Bremen
• Usable and precise asymptotics for generalized linear mixed model analysis and design AI翻译
  作者：Jiang, Jiming; Wand, Matt P.; Bhaskaran, Aishwarya
  作者单位：University of California System; University of California Davis; University of Technology Sydney
  摘要：We derive precise asymptotic results that are directly usable for confidence intervals and Wald hypothesis tests for likelihood-based generalized linear mixed model analysis. The essence of our approach is to derive the exact leading term behaviour of the Fisher information matrix when both the number of groups and number of observations within each group diverge. This leads to asymptotic normality results with simple studentizable forms. Similar analyses result in tractable leading term forms...
• The Debiased Spatial Whittle likelihood AI翻译
  作者：Guillaumin, Arthur P.; Sykulski, Adam M.; Olhede, Sofia C.; Simons, Frederik J.
  作者单位：University of London; Queen Mary University London; Lancaster University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of London; University College London; Princeton University
  摘要：We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important corrections to the well-known Whittle likelihood to account for large sources of bias caused by boundary effects and aliasing. We generalize the approach to flexibly allow for significant volumes of missing data inc...