Journal of the Royal Statistical Society: Series B

最新文章

• Sparse Kronecker product decomposition: a general framework of signal region detection in image regression AI翻译
  作者：Wu, Sanyou; Feng, Long
  作者单位：University of Hong Kong
  摘要：This paper aims to present the first Frequentist framework on signal region detection in high-resolution and high-order image regression problems. Image data and scalar-on-image regression are intensively studied in recent years. However, most existing studies on such topics focussed on outcome prediction, while the research on region detection is rather limited, even though the latter is often more important. In this paper, we develop a general framework named Sparse Kronecker Product Decompo...
• Monotone response surface of multi-factor condition: estimation and Bayes classifiers AI翻译
  作者：Cheung, Ying Kuen; Diaz, Keith M.
  作者单位：Columbia University; Columbia University
  摘要：We formulate the estimation of monotone response surface of multiple factors as the inverse of an iteration of partially ordered classifier ensembles. Each ensemble (called product-of-independent-probability-escalation (PIPE)-classifiers) is a projection of Bayes classifiers on the constrained space. We prove that the inverse of PIPE-classifiers (iPIPE) exists, and propose algorithms to efficiently compute iPIPE by reducing the space over which optimisation is conducted. The methods are applie...
• Conformalized survival analysis AI翻译
  作者：Candes, Emmanuel; Lei, Lihua; Ren, Zhimei
  作者单位：Stanford University; Stanford University; Stanford University; University of Chicago
  摘要：In this paper, we develop an inferential method based on conformal prediction, which can wrap around any survival prediction algorithm to produce calibrated, covariate-dependent lower predictive bounds on survival times. In the Type I right-censoring setting, when the censoring times are completely exogenous, the lower predictive bounds have guaranteed coverage in finite samples without any assumptions other than that of operating on independent and identically distributed data points. Under a...
• Modelling matrix time series via a tensor CP-decomposition AI翻译
  作者：Chang, Jinyuan; He, Jing; Yang, Lin; Yao, Qiwei
  作者单位：Zhejiang Gongshang University; Southwestern University of Finance & Economics - China; University of London; London School Economics & Political Science
  摘要：We consider to model matrix time series based on a tensor canonical polyadic (CP)-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions, we propose a new and one-pass estimation procedure based on a generalized eigenanalysis constructed from the serial dependence structure of the underlying process. To overcome the intricacy of solving a rank-reduced generalized eigenequation, we propose a further refined approach which projects ...
• Bayesian fusion: scalable unification of distributed statistical analyses AI翻译
  作者：Dai, Hongsheng; Pollock, Murray; Roberts, Gareth O.
  作者单位：University of Essex; Newcastle University - UK; Alan Turing Institute; University of Warwick
  摘要：There has been considerable interest in addressing the problem of unifying distributed analyses into a single coherent inference, which arises in big-data settings, when working under privacy constraints, and in Bayesian model choice. Most existing approaches relied upon approximations of the distributed analyses, which have significant shortcomings-the quality of the inference can degrade rapidly with the number of analyses being unified, and can be substantially biased when unifying analyses...
• Testing for the Markov property in time series via deep conditional generative learning AI翻译
  作者：Zhou, Yunzhe; Shi, Chengchun; Li, Lexin; Yao, Qiwei
  作者单位：University of California System; University of California Berkeley; University of London; London School Economics & Political Science; University of London; London School Economics & Political Science
  摘要：The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of paramount importance. In this article, we propose a nonparametric test for the Markov property in high-dimensional time series via deep conditional generative learning. We also apply the test sequentially to determine the order of the Markov model. We show that the test controls the type-I error asymptotically, and has...
• Manifold lifting: scaling Markov chain Monte Carlo to the vanishing noise regime AI翻译
  作者：Au, Khai Xiang; Graham, Matthew M.; Thiery, Alexandre H.
  作者单位：National University of Singapore; University of London; University College London; National University of Singapore; National University of Singapore
  摘要：Standard Markov chain Monte Carlo methods struggle to explore distributions that concentrate in the neighbourhood of low-dimensional submanifolds. This pathology naturally occurs in Bayesian inference settings when there is a high signal-to-noise ratio in the observational data but the model is inherently over-parametrised or nonidentifiable. In this paper, we propose a strategy that transforms the original sampling problem into the task of exploring a distribution supported on a manifold embe...
• Fast and fair simultaneous confidence bands for functional parameters AI翻译
  作者：Liebl, Dominik; Reimherr, Matthew
  作者单位：University of Bonn; University of Bonn; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Bonn
  摘要：Quantifying uncertainty using confidence regions is a central goal of statistical inference. Despite this, methodologies for confidence bands in functional data analysis are still underdeveloped compared to estimation and hypothesis testing. In this work, we present a new methodology for constructing simultaneous confidence bands for functional parameter estimates. Our bands possess a number of positive qualities: (1) they are not based on resampling and thus are fast to compute, (2) they are ...
• Nonparametric estimation of the continuous treatment effect with measurement error AI翻译
  作者：Huang, Wei; Zhang, Zheng
  作者单位：University of Melbourne; Renmin University of China; Renmin University of China
  摘要：We identify the average dose-response function (ADRF) for a continuously valued error-contaminated treatment by a weighted conditional expectation. We then estimate the weights nonparametrically by maximising a local generalised empirical likelihood subject to an expanding set of conditional moment equations incorporated into the deconvolution kernels. Thereafter, we construct a deconvolution kernel estimator of ADRF. We derive the asymptotic bias and variance of our ADRF estimator and provide...
• Proximal causal inference for complex longitudinal studies AI翻译
  作者：Ying, Andrew; Miao, Wang; Shi, Xu; Tchetgen, Eric J. Tchetgen
  作者单位：University of Pennsylvania; Peking University; University of Michigan System; University of Michigan; University of Pennsylvania
  摘要：A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values, also known as 'sequential randomization assumption (SRA)'. SRA is often criticized as it requires one to accurately measure all confounders. Realistically, measured covariates can rarely capture all confounders with certainty. Often covariate measurements ar...