4期

最新文章

• ART: distribution-free and model-agnostic changepoint detection with finite-sample guarantees AI翻译
  作者：Cui, Xiaolong; Geng, Haoyu; Wang, Guanghui; Wang, Zhaojun; Zou, Changliang
  作者单位：Nankai University; Nankai University
  摘要：We introduce ART, a distribution-free and model-agnostic framework for changepoint analysis with finite-sample guarantees. ART transforms independent observations into real-valued scores via a symmetric function; under the null hypothesis of no changepoint these scores are exchangeable. Ranking and aggregating the scores yields test statistics whose null distribution is known exactly from the permutation law of ranks, enabling exact finite-sample Type I error control without repeated refitting...
• Bayesian quantile estimation and regression with martingale posteriors AI翻译
  作者：Fong, Edwin; Yiu, Andrew
  作者单位：University of Hong Kong; University of Southampton
  摘要：Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior specification. We demonstrate that...
• Proximal causal inference for conditional separable effects AI翻译
  作者：Park, Chan; Stensrud, Mats J.; Tchetgen Tchetgen, Eric J.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Pennsylvania
  摘要：Scientists regularly pose questions about treatment effects on outcomes conditional on a posttreatment event. However, causal inference in such settings requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand that corresponds to scientifically meaningful questions in these settings. However, existing results for the CSE require no unmeasured confounding between the outcome and posttreatment e...
• The causal effects of modified treatment policies under network interference AI翻译
  作者：Balkus, Salvador, V; Delaney, Scott W.; Hejazi, Nima S.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcomes of others, as is common in spatial or network data. We introduce a new class of intervention-induced modified treatment policies-which we show identify such causal effects...
• Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models AI翻译
  作者：Ascolani, Filippo; Roberts, Gareth O.; Zanella, Giacomo
  作者单位：Duke University; University of Warwick; Bocconi University; Bocconi University
  摘要：We study general coordinate-wise Markov chain Monte Carlo schemes (such as Metropolis-within-Gibbs samplers), which are commonly used to fit Bayesian non-conjugate hierarchical models. We relate their convergence properties to the ones of the corresponding (potentially not implementable) random scan Gibbs sampler through the notion of conditional conductance. This allows us to study the performances of popular Metropolis-within-Gibbs schemes for non-conjugate hierarchical models, in high-dimen...
• Statistical inference for cell type deconvolution AI翻译
  作者：Xie, Dongyue; Gui, Lin; Wang, Jingshu
  作者单位：University of Chicago
  摘要：Integrating heterogeneous datasets across different measurement platforms poses fundamental challenges for statistical inference. An important example is cell type deconvolution, where cell type proportions in bulk RNA-seq data are estimated using reference single-cell data from different sources, leading to platform-specific scaling effects, measurement noise, and biological heterogeneity. Existing methods often treat estimated proportions as observed in downstream analyses, potentially compr...
• The synthetic instrument: from sparse association to sparse causation AI翻译
  作者：Tang, Dingke; Kong, Dehan; Wang, Linbo
  作者单位：University of Ottawa; University of Toronto
  摘要：In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data, such as the Lasso, assume that the associations between the exposures and the outcome are sparse. However, these methods do not estimate causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects under consideration are sparse. We show that under ...
• Skew-symmetric approximations of posterior distributions AI翻译
  作者：Pozza, Francesco; Durante, Daniele; Szabo, Botond
  作者单位：Bocconi University; Bocconi University
  摘要：Popular deterministic approximations of posterior distributions from, e.g. the Laplace method, variational Bayes and expectation-propagation, generally rely on symmetric families, often taken to be Gaussian. This choice facilitates optimization and inference, but typically affects the quality of the approximation. In fact, even in basic parametric models, posterior distributions often display asymmetries that yield bias and reduced accuracy when considering symmetric approximations. Recent res...
• Using a two-parameter sensitivity analysis framework to efficiently combine randomized and nonrandomized studies AI翻译
  作者：Yu, Ruoqi; Karmakar, Bikram; Vandeleest, Jessica; Schwarz, Eleanor Bimla
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Wisconsin System; University of Wisconsin Madison; University of California System; University of California Davis; University of California System; University of California San Francisco
  摘要：Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Randomized controlled trials (RCTs) are considered the gold standard for internal validity of inferences, whereas observational studies (OSs) often provide the opportunity for greater external validity. However, both data sources have inherent limitations preventing their use for broadly valid statistical inferences: RCTs may lack generalizability due to their selective eligibi...
• Mixing time of the conditional backward sampling particle filter AI翻译
  作者：Karjalainen, Joona; Lee, Anthony; Singh, Sumeetpal S.; Vihola, Matti
  作者单位：University of Jyvaskyla; University of Bristol; University of Wollongong
  摘要：The conditional backward sampling particle filter (CBPF) is a powerful Markov chain Monte Carlo sampler for general state space hidden Markov model (HMM) smoothing. It was proposed as an improvement over the conditional particle filter (CPF), which has an O(T2) complexity under a general 'strong' mixing assumption, where T is the time horizon. Empirical evidence of the superiority of the CBPF over the CPF has never been theoretically quantified. We show that the CBPF has O(TlogT) time complexi...