Biometrika

最新文章

• Revisiting Madigan and Mosurski: collapsibility via minimal separators AI翻译
  作者：Heng, Pei; He, Shiyuan; Sun, Yi; Guo, Jianhua
  作者单位：Northeast Normal University - China; Beijing Technology & Business University
  摘要：Collapsibility provides a principled approach to dimension reduction in contingency tables and graphical models. Madigan & Mosurski (1990) pioneered the study of minimal collapsible sets in decomposable models, but existing algorithms for general graphs remain computationally demanding. We show that a model is collapsible on to a target set precisely when that set contains at least one minimal separator between its nonadjacent vertices. This insight motivates the close minimal separator absorp...
• Integral probability metric-guided CUSUM-Net for nonparametric changepoint detection AI翻译
  作者：Li, Yunchen; Wang, Guanghui; Xu, Shuntuo; Yu, Zhou
  作者单位：East China Normal University; Nankai University; Nankai University
  摘要：We propose CUSUM-Net, a nonparametric method for changepoint detection based on integral probability metrics and deep neural networks. Our approach learns a critic function by maximizing an aggregate CUSUM objective over candidate changepoints, thereby linking changepoint detection to optimization of two-sample integral probability metrics. The learned critic induces a one-dimensional representation on which changepoints are localized by a classical CUSUM scan. Unlike parametric procedures, CU...
• Design stability in adaptive experiments: implications for treatment effect estimation AI翻译
  作者：Sengupta, Saikat; Khamaru, Koulik; Ghosh, Suvrojit; Dasgupta, Tirthankar
  作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Rutgers University System; Rutgers University New Brunswick
  摘要：We study the problem of estimating the average treatment effect under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized designs, the setting we consider is one in which the probability of assigning treatment to each experimental unit may depend on prior assignments and observed outcomes. Within the potential outcomes framework (), we propose and analyse two natural estimators for the average treatment effect: the inverse propensity weighted e...
• Estimating the number of significant components in high-dimensional principal component analysis AI翻译
  作者：Zhang, Bo; Zhang, Zhixiang; Pan, Guangming
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Macau; Nanyang Technological University
  摘要：We consider the problem of estimating the number of significant components in high-dimensional principal component analysis. We propose a new penalized approach using the explained variance ratio and the rigidity of the nonspiked sample eigenvalues of sample covariance matrices of $ p $ variables. Compared with methods in the existing literature, the consistency of the proposed estimator holds, not only for independent data, but also for some times series data when the dimension $ p $ and the ...
• On the degrees of freedom of the smoothing parameter AI翻译
  作者：Saefken, B.; Kneib, T.; Wood, S. N.
  作者单位：TU Clausthal; University of Gottingen; University of Edinburgh
  摘要：The smoothing parameters in a semiparametric model are estimated based on criteria such as generalized cross-validation or restricted maximum likelihood. As these parameters are estimated in a data-driven manner, they influence the degrees of freedom of a semiparametric model, based on Stein's lemma. This allows us to associate parts of the degrees of freedom of a semiparametric model with the smoothing parameters. A framework is introduced that enables these degrees of freedom of the smoothin...
• High-dimensional factor analysis for network-linked data AI翻译
  作者：Li, Jinming; Xu, Gongjun; Zhu, Ji
  作者单位：University of Michigan System; University of Michigan
  摘要：Factor analysis is a statistical tool widely used in many disciplines, such as psychology, economics and sociology. As observations linked by networks become increasingly common, incorporating network structures into factor analysis is an important problem that remains open. This article focuses on high-dimensional factor analysis involving network-connected observations, and we propose a generalized factor model with latent factors that account for both the network structure and the dependenc...
• Pseudolikelihood estimators for graphical models: existence and uniqueness AI翻译
  作者：Roycraft, B.; Rajaratnam, B.
  作者单位：State University System of Florida; University of Florida; University of California System; University of California Davis
  摘要：Graphical and sparse (inverse) covariance models have found widespread use in modern sample-starved high-dimensional applications. A part of their wide appeal stems from the significantly low sample sizes required for existence of the estimators, especially in comparison with the classical full covariance model. For undirected Gaussian graphical models, the minimum sample size required for the existence of maximum likelihood estimators had been an open question for almost half a century, and h...
• Priors for second-order unbiased Bayes estimators AI翻译
  作者：Sakai, Mana; Matsuda, Takeru; Kubokawa, Tatsuya
  作者单位：University of Tokyo; University of Tokyo
  摘要：Asymptotically unbiased priors, introduced by Hartigan (1965), are designed to achieve second-order unbiasedness of Bayes estimators. This paper extends Hartigan's framework to non-independent-and-identically-distributed models by deriving a system of partial differential equations that characterizes asymptotically unbiased priors. Furthermore, we establish a necessary and sufficient condition for the existence of such priors and propose a simple procedure for constructing them. The proposed m...
• Infinite joint species distribution models AI翻译
  作者：Stolf, F.; Dunson, D. B.
  作者单位：Duke University
  摘要：Joint species distribution models are popular in ecology for modelling covariate effects on species occurrence, while characterizing cross-species dependence. Data consist of multivariate binary indicators of the occurrences of different species in each sample, along with sample-specific covariates. A key problem is that current models implicitly assume that the list of species under consideration is predefined and finite, while for highly diverse groups of organisms, it is impossible to antic...
• Doubly robust and heteroscedasticity-aware sample trimming for causal inference AI翻译
  作者：Khan, S.; Ugander, J.
  作者单位：Stanford University; Stanford University
  摘要：A popular method for variance reduction in causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Crump et al., 2009; Yang & Ding, 2018), but in modern settings where heteroscedastic data are analysed with nonparametric models, existing theory fails to support current practice. In this work, we address this challenge b...