3期

最新文章

• 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 ...
• 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...
• Causal inference with invalid instruments: post-selection problems and a solution using searching and sampling AI翻译
  作者：Guo, Zijian
  作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University New Brunswick
  摘要：Instrumental variable methods are among the most commonly used causal inference approaches to deal with unmeasured confounders in observational studies. The presence of invalid instruments is the primary concern for practical applications, and a fast-growing area of research is inference for the causal effect with possibly invalid instruments. This paper illustrates that the existing confidence intervals may undercover when the valid and invalid instruments are hard to separate in a data-depen...
• Exact Monte Carlo likelihood-based inference for jump-diffusion processes AI翻译
  作者：Goncalves, Flavio B.; Latuszynski, Krzysztof; Roberts, Gareth O.
  作者单位：Universidade Federal de Minas Gerais; University of Warwick
  摘要：Statistical inference for discretely observed jump-diffusion processes is a complex problem which motivates new methodological challenges. Thus, existing approaches invariably resort to time-discretisations which inevitably lead to approximations in inference. In this paper, we give the first general collection of methodologies for exact (in this context meaning discretisation-free) likelihood-based inference for discretely observed finite activity jump-diffusions. The only sources of error in...
• Ordering factorial experiments (Apr, qkad027, 2023) AI翻译
  作者：Yang, Liuqing; Zhou, Yongdao; Liu, Min-Qian
• Permutation-based true discovery guarantee by sum tests AI翻译
  作者：Vesely, Anna; Finos, Livio; Goeman, Jelle J.
  作者单位：University of Bremen; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Padua; Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC)
  摘要：Sum-based global tests are highly popular in multiple hypothesis testing. In this paper, we propose a general closed testing procedure for sum tests, which provides lower confidence bounds for the proportion of true discoveries (TDPs), simultaneously over all subsets of hypotheses. These simultaneous inferences come for free, i.e., without any adjustment of the alpha-level, whenever a global test is used. Our method allows for an exploratory approach, as simultaneity ensures control of the TDP...
• Computationally efficient and data-adaptive changepoint inference in high dimension AI翻译
  作者：Wang, Guanghui; Feng, Long
  作者单位：East China Normal University; East China Normal University; Nankai University; Nankai University
  摘要：High-dimensional changepoint inference that adapts to various change patterns has received much attention recently. We propose a simple, fast yet effective approach for adaptive changepoint testing. The key observation is that two statistics based on aggregating cumulative sum statistics over all dimensions and possible changepoints by taking their maximum and summation, respectively, are asymptotically independent under some mild conditions. Hence, we are able to form a new test by combining ...
• Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation AI翻译
  作者：Yang, Shu; Gao, Chenyin; Zeng, Donglin; Wang, Xiaofei
  作者单位：North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; Duke University
  摘要：We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternat...
• Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution AI翻译
  作者：Li, Wei; Miao, Wang; Tchetgen, Eric Tchetgen
  作者单位：Renmin University of China; Renmin University of China; Peking University; University of Pennsylvania
  摘要：We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for vn-estimability of the mean functional. This condition naturally strengthens the identifying condition, and it requires ...