2017

最新文章

• Regression models on Riemannian symmetric spaces AI翻译
  作者：Cornea, Emil; Zhu, Hongtu; Kim, Peter; Ibrahim, Joseph G.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Guelph
  摘要：The paper develops a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arise frequently in medical imaging, surface modelling and computer vision, among many other fields. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distri...
• Classification of non-parametric regression functions in longitudinal data models AI翻译
  作者：Vogt, Michael; Linton, Oliver
  作者单位：University of Bonn; University of Cambridge
  摘要：We investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the data. Moreover, we derive the asym...
• 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...
• Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation AI翻译
  作者：Chaudhuri, Sanjay; Mondal, Debashis; Yin, Teng
  作者单位：National University of Singapore; Oregon State University
  摘要：We consider Bayesian empirical likelihood estimation and develop an efficient Hamiltonian Monte Carlo method for sampling from the posterior distribution of the parameters of interest. The method proposed uses hitherto unknown properties of the gradient of the underlying log-empirical-likelihood function. We use results from convex analysis to show that these properties hold under minimal assumptions on the parameter space, prior density and the functions used in the estimating equations deter...
• 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...
• EigenPrism: inference for high dimensional signal-to-noise ratios AI翻译
  作者：Janson, Lucas; Barber, Rina Foygel; Candes, Emmanuel
  作者单位：Stanford University; University of Chicago
  摘要：Consider the following three important problems in statistical inference: constructing confidence intervals for the error of a high dimensional (p>n) regression estimator, the linear regression noise level and the genetic signal-to-noise ratio of a continuous-valued trait ( related to the heritability). All three problems turn out to be closely related to the little-studied problem of performing inference on the (l)2-norm of the signal in high dimensional linear regression. We derive a novel p...
• Principal stratification analysis using principal scores AI翻译
  作者：Ding, Peng; Lu, Jiannan
  作者单位：University of California System; University of California Berkeley; Microsoft
  摘要：Practitioners are interested in not only the average causal effect of a treatment on the outcome but also the underlying causal mechanism in the presence of an intermediate variable between the treatment and outcome. However, in many cases we cannot randomize the intermediate variable, resulting in sample selection problems even in randomized experiments. Therefore, we view randomized experiments with intermediate variables as semiobservational studies. In parallel with the analysis of observa...
• 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...