Biometrika

最新文章

• On the inconsistency of matching without replacement AI翻译
  作者：Savje, F.
  作者单位：Yale University
  摘要：The paper shows that matching without replacement on propensity scores produces estimators that generally are inconsistent for the average treatment effect of the treated. To achieve consistency, practitioners must either assume that no units exist with propensity scores greater than 1/2 or assume that there is no confounding among such units. The result is not driven by the use of propensity scores, and similar artifacts arise when matching on other scores as long as it is without replacement.
• Determining the number of factors in high-dimensional generalized latent factor models AI翻译
  作者：Chen, Y.; Li, X.
  作者单位：University of London; London School Economics & Political Science; University of Minnesota System; University of Minnesota Twin Cities
  摘要：As a generalization of the classical linear factor model, generalized latent factor models are useful for analysing multivariate data of different types, including binary choices and counts. This paper proposes an information criterion to determine the number of factors in generalized latent factor models. The consistency of the proposed information criterion is established under a high-dimensional setting, where both the sample size and the number of manifest variables grow to infinity, and d...
• Uniqueness and global optimality of the maximum likelihood estimator for the generalized extreme value distribution AI翻译
  作者：Zhang, Likun; Shaby, Benjamin A.
  作者单位：United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Colorado State University System; Colorado State University Fort Collins
  摘要：The three-parameter generalized extreme value distribution arises from classical univariate extreme value theory, and is in common use for analysing the far tail of observed phenomena, yet important asymptotic properties of likelihood-based estimation under this standard model have not been established. In this paper we prove that the maximum likelihood estimator is global and unique. An interesting secondary result entails the uniform consistency of a class of limit relations in a tight neigh...
• Functional hybrid factor regression model for handling heterogeneity in imaging studies AI翻译
  作者：Huang, C.; Zhu, H.
  作者单位：State University System of Florida; Florida State University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer's disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or m...
• Identifiability of causal effects with multiple causes and a binary outcome AI翻译
  作者：Kong, Dehan; Yang, Shu; Wang, Linbo
  作者单位：University of Toronto; North Carolina State University
  摘要：Unobserved confounding presents a major threat to causal inference in observational studies. Recently, several authors have suggested that this problem could be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that the causal effect is indeed i...
• Efficient adjustment sets in causal graphical models with hidden variables AI翻译
  作者：Smucler, E.; Sapienza, F.; Rotnitzky, A.
  作者单位：University of California System; University of California Berkeley; Universidad Torcuato Di Tella
  摘要：We study the selection of adjustment sets for estimating the interventional mean under a point exposure dynamic treatment regime, that is, a treatment rule that depends on the subject's covariates. We assume a nonparametric causal graphical model with, possibly, hidden variables and at least one adjustment set comprised of observable variables. We provide the definition of a valid adjustment set for a point exposure dynamic treatment regime, which generalizes the existing definition for a stat...
• Partial separability and functional graphical models for multivariate Gaussian processes AI翻译
  作者：ZAPATA, J.; OH, S. Y.; PETERSEN, A.
  作者单位：University of California System; University of California Santa Barbara
  摘要：The covariance structure of multivariate functional data can be highly complex, especially if the multivariate dimension is large, making extensions of statistical methods for standard multivariate data to the functional data setting challenging. For example, Gaussian graphical models have recently been extended to the setting of multivariate functional data by applying multivariate methods to the coefficients of truncated basis expansions. However, compared with multivariate data, a key diffi...
• Searching for robust associations with a multi-environment knockoff filter AI翻译
  作者：Li, S.; Sesia, M.; Romano, Y.; Candes, E.; Sabatti, C.
  作者单位：Stanford University; University of Southern California; Technion Israel Institute of Technology
  摘要：In this article we develop a method based on model-X knockoffs to find conditional associations that are consistent across environments, while controlling the false discovery rate. The motivation for this problem is that large datasets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations replicated under different conditions may be more interesting. In fact, sometimes consist...
• Risk bounds for quantile trend filtering AI翻译
  作者：Padilla, Oscar Hernan Madrid; Chatterjee, Sabyasachi
  作者单位：University of California System; University of California Los Angeles; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We study quantile trend filtering, a recently proposed method for nonparametric quantile regression, with the goal of generalizing existing risk bounds for the usual trend-filtering estimators that perform mean regression. We study both the penalized and the constrained versions, of order r >= 1, of univariate quantile trend filtering. Our results show that both the constrained and the penalized versions of order r >= 1 attain the minimax rate up to logarithmic factors, when the (r - 1)th disc...
• Asymptotics of sample tail autocorrelations for tail-dependent time series: phase transition and visualization AI翻译
  作者：Zhang, Ting
  作者单位：University System of Georgia; University of Georgia
  摘要：In this article we develop an asymptotic theory for sample tail autocorrelations of time series data that can exhibit serial dependence in both tail and non-tail regions. Unlike with the traditional autocorrelation function, the study of tail autocorrelations requires a double asymptotic scheme to capture the tail phenomena, and our results do not impose any restrictions on the dependence structure in non-tail regions and allow processes that are not necessarily strongly mixing. The newly deve...