2022

最新文章

• Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-differentiable Priors AI翻译
  作者：Goldman, Jacob Vorstrup; Sell, Torben; Singh, Sumeetpal Sidhu
  作者单位：University of Cambridge; University of Cambridge
  摘要：The use of nondifferentiable priors in Bayesian statistics has become increasingly popular, in particular in Bayesian imaging analysis. Current state-of-the-art methods are approximate in the sense that they replace the posterior with a smooth approximation via Moreau-Yosida envelopes, and apply gradient-based discretized diffusions to sample from the resulting distribution. We characterize the error of the Moreau-Yosida approximation and propose a novel implementation using underdamped Langev...
• Survival Regression Models With Dependent Bayesian Nonparametric Priors AI翻译
  作者：Riva-Palacio, Alan; Leisen, Fabrizio; Griffin, Jim
  作者单位：Universidad Nacional Autonoma de Mexico; University of Nottingham; University of London; University College London
  摘要：We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent ve...
• Center-Outward R-Estimation for Semiparametric VARMA Models AI翻译
  作者：Hallin, M.; La Vecchia, D.; Liu, H.
  作者单位：Universite Libre de Bruxelles; University of Geneva; Lancaster University
  摘要：We propose a new class of R-estimators for semiparametric VARMA models in which the innovation density plays the role of the nuisance parameter. Our estimators are based on the novel concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam's asymptotic theory of statistical experiments, yield a class of semiparametric estimation procedures, which are efficient (at a given reference density), root-n consistent, and asymptotically normal under a ...
• Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models AI翻译
  作者：Bai, Ray; Moran, Gemma E.; Antonelli, Joseph L.; Chen, Yong; Boland, Mary R.
  作者单位：University of South Carolina System; University of South Carolina Columbia; Columbia University; State University System of Florida; University of Florida; University of Pennsylvania
  摘要：We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse generalized additive models (GAMs), thereby introducing the first nonparametric variant of the spike-and-slab lasso methodology. Our model simultaneously performs group selection and estimation, while our fully Bayes treatment of the mixture proportion allows for model complexity control and automatic self-adaptivity...
• Modeling and Learning From Variation and Covariation AI翻译
  作者：McShane, Blakeley B.; Bockenholt, Ulf; Hansen, Karsten T.
  作者单位：Northwestern University; University of California System; University of California San Diego
• On the Hauck-Donner Effect in Wald Tests: Detection, Tipping Points, and Parameter Space Characterization AI翻译
  作者：Yee, Thomas W.
  作者单位：University of Auckland
  摘要：The Wald test remains ubiquitous in statistical practice despite shortcomings such as its inaccuracy in small samples and lack of invariance under reparameterization. This article develops on another but lesser-known shortcoming called the Hauck-Donner effect (HDE) whereby a Wald test statistic is no longer monotone increasing as a function of increasing distance between the parameter estimate and the null value. Resulting in an upward biased p-value and loss of power, the aberration can lead ...
• Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model AI翻译
  作者：Malinsky, Daniel; Shpitser, Ilya; Tchetgen, Eric J. Tchetgen
  作者单位：Columbia University; Johns Hopkins University; University of Pennsylvania
  摘要：We study the identification and estimation of statistical functionals of multivariate data missing nonmonotonically and not-at-random, taking a semiparametric approach. Specifically, we assume that the missingness mechanism satisfies what has been previously called no self-censoring or itemwise conditionally independent nonresponse, which roughly corresponds to the assumption that no partially observed variable directly determines its own missingness status. We show that this assumption, combi...
• On Design Orthogonality, Maximin Distance, and Projection Uniformity for Computer Experiments AI翻译
  作者：Wang, Yaping; Sun, Fasheng; Xu, Hongquan
  作者单位：East China Normal University; Northeast Normal University - China; Northeast Normal University - China; University of California System; University of California Los Angeles
  摘要：Space-filling designs are widely used in both computer and physical experiments. Column-orthogonality, maximin distance, and projection uniformity are three basic and popular space-filling criteria proposed from different perspectives, but their relationships have been rarely investigated. We show that the average squared correlation metric is a function of the pairwiseL(2)-distances between the rows only. We further explore the connection between uniform projection designs and maximinL(1)-dis...
• Detection of Local Differences in Spatial Characteristics Between Two Spatiotemporal Random Fields AI翻译
  作者：Yun, Sooin; Zhang, Xianyang; Li, Bo
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Texas A&M University System; Texas A&M University College Station
  摘要：Comparing the spatial characteristics of spatiotemporal random fields is often at demand. However, the comparison can be challenging due to the high-dimensional feature and dependency in the data. We develop a new multiple testing approach to detect local differences in the spatial characteristics of two spatiotemporal random fields by taking the spatial information into account. Our method adopts a two-component mixture model for location wisep-values and then derives a new false discovery ra...
• Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface AI翻译
  作者：Ma, Tianwen; Li, Yang; Huggins, Jane E.; Zhu, Ji; Kang, Jian
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common nontarget stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the proba...