Journal of the American Statistical Association

最新文章

• Latent Multimodal Functional Graphical Model Estimation AI翻译
  作者：Tsai, Katherine; Zhao, Boxin; Koyejo, Sanmi; Kolar, Mladen
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago; Stanford University
  摘要：Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences. One prominent motivation to acquire such data is to enable new discoveries of the underlying connectivity by combining multimodal signals. Despite the scientific interest, there remains a gap in principled statistical methods f...
• Semiparametric Bayesian Inference for Local Extrema of Functions in the Presence of Noise AI翻译
  作者：Li, Meng; Liu, Zejian; Yu, Cheng-Han; Vannucci, Marina
  作者单位：Rice University; Marquette University
  摘要：There is a wide range of applications where the local extrema of a function are the key quantity of interest. However, there is surprisingly little work on methods to infer local extrema with uncertainty quantification in the presence of noise. By viewing the function as an infinite-dimensional nuisance parameter, a semiparametric formulation of this problem poses daunting challenges, both methodologically and theoretically, as (i) the number of local extrema may be unknown, and (ii) the induc...
• A General Framework for Circular Local Likelihood Regression AI翻译
  作者：Alonso-Pena, Maria; Gijbels, Irene; Crujeiras, Rosa M.
  作者单位：KU Leuven; Universidade de Santiago de Compostela; KU Leuven; KU Leuven
  摘要：This article presents a general framework for the estimation of regression models with circular covariates, where the conditional distribution of the response given the covariate can be specified through a parametric model. The estimation of a conditional characteristic is carried out nonparametrically, by maximizing the circular local likelihood, and the estimator is shown to be asymptotically normal. The problem of selecting the smoothing parameter is also addressed, as well as bias and vari...
• The Cellwise Minimum Covariance Determinant Estimator AI翻译
  作者：Raymaekers, Jakob; Rousseeuw, Peter J.
  作者单位：Maastricht University; KU Leuven
  摘要：The usual Minimum Covariance Determinant (MCD) estimator of a covariance matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) that behave differently from the majority of cases, raising suspicion that they might belong to a different population. On the other hand, cellwise outliers are individual cells in the data matrix. When a row contains one or more outlying cells, the other cells in the same row still contain useful information that we wish to pre...
• Discovery and Inference of a Causal Network with Hidden Confounding AI翻译
  作者：Chen, Li; Li, Chunlin; Shen, Xiaotong; Pan, Wei
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Iowa State University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental variable estimatio...
• A Randomized Pairwise Likelihood Method for Complex Statistical Inferences AI翻译
  作者：Mazo, Gildas; Karlis, Dimitris; Rau, Andrea
  作者单位：INRAE; Universite Paris Saclay; Athens University of Economics & Business; INRAE; Universite Paris Saclay; AgroParisTech; Universite de Lille; Universite de Picardie Jules Verne (UPJV); INRAE
  摘要：Pairwise likelihood methods are commonly used for inference in parametric statistical models in cases where the full likelihood is too complex to be used, such as multivariate count data. Although pairwise likelihood methods represent a useful solution to perform inference for intractable likelihoods, several computational challenges remain. The pairwise likelihood function still requires the computation of a sum over all pairs of variables and all observations, which may be prohibitive in hig...
• Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness AI翻译
  作者：Li, Kendrick Qijun; Shi, Xu; Miao, Wang; Tchetgen, Eric Tchetgen
  作者单位：University of Michigan System; University of Michigan; Peking University; University of Pennsylvania
  摘要：The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as Influenza, Rotavirus, Dengue fever, and more recently COVID-19. In a TND study, individuals who experience symptoms and seek care are recruited and tested for the infectious disease which defines cases and controls. Despite TND's potential to reduce unobserved differences in healthcare seeking behavior (HSB) between va...
• Large-Scale Two-Sample Comparison of Support Sets AI翻译
  作者：Geng, Haoyu; Cui, Xiaolong; Ren, Haojie; Zou, Changliang
  作者单位：Nankai University; Nankai University; Shanghai Jiao Tong University
  摘要：Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. The article takes a different perspective and investigates the null hypotheses that the two support sets are equal. This formulation of the testing problem is motivated by the fact that in many applications where the two parameter vectors being compared are both sparse, one might be more concerned about the detection of differential sparsity structures ra...
• Anytime-Valid Tests of Conditional Independence Under Model-X AI翻译
  作者：Grunwald, Peter; Henzi, Alexander; Lardy, Tyron
  作者单位：Leiden University - Excl LUMC; Leiden University; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：We propose a sequential, anytime-valid method to test the conditional independence of a response Y and a predictor X given a random vector Z. The proposed test is based on e-statistics and test martingales, which generalize likelihood ratios and allow valid inference at arbitrary stopping times. In accordance with the recently introduced model-X setting, our test depends on the availability of the conditional distribution of X given Z, or at least a sufficiently sharp approximation thereof. Wi...
• Bayesian Conditional Transformation Models AI翻译
  作者：Carlan, Manuel; Kneib, Thomas; Klein, Nadja
  作者单位：University of Gottingen; Dortmund University of Technology
  摘要：Recent developments in statistical regression methodology shift away from pure mean regression towards distributional regression models. One important strand thereof is that of conditional transformation models (CTMs). CTMs infer the entire conditional distribution directly by applying a transformation function to the response conditionally on a set of covariates towards a simple log-concave reference distribution. Thereby, CTMs allow not only variance, kurtosis or skewness but the complete co...