2023

最新文章

• Vintage factor analysis with Varimax performs statistical inference AI翻译
  作者：Zhou, Ying; Zhang, Xinyi
  作者单位：University of Toronto
• Debiased and thresholded ridge regression for linear models with heteroskedastic and correlated errors AI翻译
  作者：Zhang, Yunyi; Politis, Dimitris N.
  作者单位：The Chinese University of Hong Kong, Shenzhen; University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：High-dimensional linear models with independent errors have been well-studied. However, statistical inference on a high-dimensional linear model with heteroskedastic, dependent (and possibly nonstationary) errors is still a novel topic. Under such complex assumptions, the paper at hand introduces a debiased and thresholded ridge regression estimator that is consistent, and is able to recover the model sparsity. Moreover, we derive a Gaussian approximation theorem for the estimator, and apply a...
• Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：He, Yinqiu; Gu, Yuqi; Ying, Zhiliang
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Columbia University; Columbia University
• Semi-supervised approaches to efficient evaluation of model prediction performance (vol 80, pg 579, 2018) AI翻译
  作者：Gronsbell, Jessica L.; Cai, Tianxi
• Root and community inference on the latent growth process of a network AI翻译
  作者：Crane, Harry; Xu, Min
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erd & odblac;s-R & eacute;nyi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erd & odblac;s-R & eacute;nyi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER comp...
• Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (vol 80, pg 157, 2018) AI翻译
  作者：Goncalves, Flavio B.; Gamerman, Dani
  作者单位：Universidade Federal de Minas Gerais; Universidade Federal do Rio de Janeiro
• Biased-sample empirical likelihood weighting for missing data problems: an alternative to inverse probability weighting AI翻译
  作者：Liu, Yukun; Fan, Yan
  作者单位：East China Normal University; Shanghai University of International Business & Economics
  摘要：Inverse probability weighting (IPW) is widely used in many areas when data are subject to unrepresentativeness, missingness, or selection bias. An inevitable challenge with the use of IPW is that the IPW estimator can be remarkably unstable if some probabilities are very close to zero. To overcome this problem, at least three remedies have been developed in the literature: stabilizing, thresholding, and trimming. However, the final estimators are still IPW-type estimators, and inevitably inher...
• Informative core identification in complex networks AI翻译
  作者：Miao, Ruizhong; Li, Tianxi
  作者单位：University of Virginia
  摘要：In a complex network, the core component with interesting structures is usually hidden within noninformative connections. The noises and bias introduced by the noninformative component can obscure the salient structure and limit many network modeling procedures' effectiveness. This paper introduces a novel core-periphery model for the noninformative periphery structure of networks without imposing a specific form of the core. We propose spectral algorithms for core identification for general d...
• 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...
• On inference in high-dimensional regression AI翻译
  作者：Battey, Heather S.; Reid, Nancy
  作者单位：Imperial College London; University of Toronto
  摘要：This paper develops an approach to inference in a linear regression model when the number of potential explanatory variables is larger than the sample size. The approach treats each regression coefficient in turn as the interest parameter, the remaining coefficients being nuisance parameters, and seeks an optimal interest-respecting transformation, inducing sparsity on the relevant blocks of the notional Fisher information matrix. The induced sparsity is exploited through a marginal least-squa...