5期

最新文章

• Robust estimation of encouragement design intervention effects transported across sites AI翻译
  作者：Rudolph, Kara E.; van der Laan, Mark J.
  作者单位：University of California System; University of California Berkeley; University of California System; University of California San Francisco
  摘要：We develop robust targeted maximum likelihood estimators (TMLEs) for transporting intervention effects from one population to another. Specifically, we develop TMLEs for three transported estimands: the intent-to-treat average treatment effect (ATE) and complier ATE, which are relevant for encouragement design interventions and instrumental variable analyses, and the ATE of the exposure on the outcome, which is applicable to any randomized or observational study. We demonstrate finite sample p...
• Joint non-parametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error AI翻译
  作者：Wang, Ching-Yun; Cullings, Harry; Song, Xiao; Kopecky, Kenneth J.
  作者单位：Fred Hutchinson Cancer Center; Radiation Effects Research Foundation - Japan; University System of Georgia; University of Georgia
  摘要：Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. We investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error mo...
• Quantile spectral analysis for locally stationary time series AI翻译
  作者：Birr, Stefan; Volgushev, Stanislav; Kley, Tobias; Dette, Holger; Hallin, Marc
  作者单位：Ruhr University Bochum; University of Toronto; University of London; London School Economics & Political Science; Universite Libre de Bruxelles
  摘要：Classical spectral methods are subject to two fundamental limitations: they can account only for covariance-related serial dependences, and they require second-order stationarity. Much attention has been devoted lately to quantile-based spectral methods that go beyond covariance-based serial dependence features. At the same time, covariance-based methods relaxing stationarity into much weaker local stationarity conditions have been developed for a variety of time series models. Here, we combin...
• Rank-based procedures in factorial designs: hypotheses about non-parametric treatment effects AI翻译
  作者：Brunner, Edgar; Konietschke, Frank; Pauly, Markus; Puri, Madan L.
  作者单位：University of Gottingen; University of Texas System; University of Texas Dallas; Ulm University; Indiana University System; Indiana University Bloomington
  摘要：Existing tests for factorial designs in the non-parametric case are based on hypotheses formulated in terms of distribution functions. Typical null hypotheses, however, are formulated in terms of some parameters or effect measures, particularly in heteroscedastic settings. Here this idea is extended to non-parametric models by introducing a novel non-parametric analysis-of-variance type of statistic based on ranks or pseudoranks which is suitable for testing hypotheses formulated in meaningful...
• Parameter stability and semiparametric inference in time varying auto-regressive conditional heteroscedasticity models AI翻译
  作者：Truquet, Lionel
  作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
  摘要：We develop a complete methodology for detecting time varying or non-time-varying parameters in auto-regressive conditional heteroscedasticity (ARCH) processes. For this, we estimate and test various semiparametric versions of time varying ARCH models which include two well-known non-stationary ARCH-type models introduced in the econometrics literature. Using kernel estimation, we show that non-time-varying parameters can be estimated at the usual parametric rate of convergence and, for Gaussia...
• Concordance-assisted learning for estimating optimal individualized treatment regimes AI翻译
  作者：Fan, Caiyun; Lu, Wenbin; Song, Rui; Zhou, Yong
  作者单位：Shanghai University of International Business & Economics; North Carolina State University; Shanghai University of Finance & Economics; Chinese Academy of Sciences
  摘要：We propose new concordance-assisted learning for estimating optimal individualized treatment regimes. We first introduce a type of concordance function for prescribing treatment and propose a robust rank regression method for estimating the concordance function. We then find treatment regimes, up to a threshold, to maximize the concordance function, named the prescriptive index. Finally, within the class of treatment regimes that maximize the concordance function, we find the optimal threshold...
• Estimation of the common mean from heterogeneous normal observations with unknown variances AI翻译
  作者：Rukhin, Andrew L.
  作者单位：National Institute of Standards & Technology (NIST) - USA
  摘要：To determine the common mean of heterogeneous normal observations, the Bayes procedures and the invariant maximum likelihood estimators of the weights forming the weighted means statistic are obtained when there are no variance estimates. The Bayes statistic is based on the reference, Geisser-Cornfield prior distribution which makes the posterior (discrete) distribution of the mean to be supported by the observed data with probabilities determined via the geometric means of the distances betwe...
• Sparse graphs using exchangeable random measures AI翻译
  作者：Caron, Francois; Fox, Emily B.
  作者单位：University of Oxford; University of Washington; University of Washington Seattle
  摘要：Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this arraywhich can aid in modelling, computations and theoretical analysisthe Aldous-Hoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable random measure and appeal to the Kallenberg representation theorem for this object. We explore using completely rand...
• Testing and confidence intervals for high dimensional proportional hazards models AI翻译
  作者：Fang, Ethan X.; Ning, Yang; Liu, Han
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Cornell University; Princeton University
  摘要：The paper considers the problem of hypothesis testing and confidence intervals in high dimensional proportional hazards models. Motivated by a geometric projection principle, we propose a unified likelihood ratio inferential framework, including score, Wald and partial likelihood ratio statistics for hypothesis testing. Without assuming model selection consistency, we derive the asymptotic distributions of these test statistics, establish their semiparametric optimality and conduct power analy...
• Convex clustering via l1 fusion penalization AI翻译
  作者：Radchenko, Peter; Mukherjee, Gourab
  作者单位：University of Southern California; University of Sydney
  摘要：We study the large sample behaviour of a convex clustering framework, which minimizes the sample within cluster sum of squares under an l(1) fusion constraint on the cluster centroids. This recently proposed approach has been gaining in popularity; however, its asymptotic properties have remained mostly unknown. Our analysis is based on a novel representation of the sample clustering procedure as a sequence of cluster splits determined by a sequence of maximization problems. We use this repres...