Journal of the Royal Statistical Society: Series B

最新文章

• Bounds for the regression parameters in dependently censored survival models AI翻译
  作者：Willems, Ilias; Beyhum, Jad; Van Keilegom, Ingrid
  作者单位：KU Leuven; KU Leuven
  摘要：We propose a semiparametric model to study the effect of covariates on the distribution of a censored event time while making minimal assumptions about the censoring mechanism. The model is partially identified, and we obtain bounds on the covariate effects which are allowed to be time-dependent. Moreover, these bounds can be interpreted as classical confidence intervals and are obtained by aggregating information in the conditional Peterson bounds over the covariate space. As a special case, ...
• False discovery rate control with compound p-values AI翻译
  作者：Barber, Rina Foygel; Samworth, Richard J.
  作者单位：University of Chicago; University of Cambridge
  摘要：In the setting of multiple testing, compound p-values generalize p-values by asking for superuniformity to hold only on average across all true nulls. We study the properties of the Benjamini-Hochberg procedure applied to compound p-values. Under independence, we show that the false discovery rate (FDR) is at most 1.93 alpha, where alpha is the nominal level, and exhibit a distribution for which the FDR is 76 alpha. If additionally all nulls are true, then the upper bound can be improved to al...
• Dr Arun Chind's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Chind, Arun Peter
• Alexander Modell's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Modell, Alexander
  作者单位：Imperial College London
• Online kernel CUSUM for change-point detection AI翻译
  作者：Wei, Song; Xie, Yao
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：We present a computationally efficient online kernel Cumulative Sum method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location. Our approach exhibits increased sensitivity to small changes compared to existing kernel-based change-point detection methods, including the Scan-B statistic, corresponding to a non-parametric Shewhart chart-type procedure. We provide accurate analytic approximations for two key performa...
• Harmonized estimation of subgroup-specific treatment effects in randomized trials: the use of external control data AI翻译
  作者：Schwartz, Daniel; Saha, Riddhiman; Ventz, Steffen; Trippa, Lorenzo
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. In particular, subgroup analyses of early-stage trials often influence the design and eligibility criteria of subsequent confirmatory trials and ultimately influence which subpopulations will receive the treatment after regulatory approval. However, subgroup analyses are often complicated by small sample sizes, which leads to substantial uncertainty ...
• Covariate-assisted bounds on causal effects with instrumental variables AI翻译
  作者：Levis, Alexander W.; Bonvini, Matteo; Zeng, Zhenghao; Keele, Luke; Kennedy, Edward H.
  作者单位：Carnegie Mellon University; University of Pennsylvania
  摘要：When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the marginal average treatment effect (ATE) from IVs relies on strong untestable structural assumptions. When one is unwilling to assert such structure, IVs can nonetheless be used to construct bounds on the ATE. Famously, Alexander Balke and Judea Pearl proved tight bounds on the ATE for a binary outcome, in a randomiz...
• Causal K-means clustering AI翻译
  作者：Kim, Kwangho; Kim, Jisu; Kennedy, Edward H.
  作者单位：Korea University; Seoul National University (SNU); Carnegie Mellon University
  摘要：Causal effects are often characterized at the population level, which can mask important heterogeneity across latent subgroups. Since the subgroup structure is unknown, identifying and evaluating subgroup specific effects is substantially more challenging than standard population level analysis. We address this problem by proposing Causal k-Means Clustering, a framework that uses k-means clustering ideas to recover unknown subgroup structure from individual level causal contrasts. The problem ...
• Model privacy: a unified framework for understanding model stealing attacks and defences AI翻译
  作者：Wang, Ganghua; Yang, Yuhong; Ding, Jie
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：The use of machine learning (ML) has become increasingly prevalent in various domains, highlighting the importance of understanding and ensuring its safety. One pressing concern is the vulnerability of ML applications to model stealing attacks. These attacks involve adversaries attempting to recover a learned model through limited query-response interactions, such as those found in cloud-based services or on-chip artificial intelligence interfaces. While existing literature proposes various at...
• Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods (vol 85,pg 1173,2023) AI翻译
  作者：Whitehouse, Michael; Whiteley, Nick; Rimella, Lorenzo