Journal of the Royal Statistical Society: Series B

最新文章

• Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting AI翻译
  作者：Guo, Zijian; Kang, Hyunseung; Cai, T. Tony; Small, Dylan S.
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania
  摘要：A major challenge in instrumental variable (IV) analysis is to find instruments that are valid, or have no direct effect on the outcome and are ignorable. Typically one is unsure whether all of the putative IVs are in fact valid. We propose a general inference procedure in the presence of invalid IVs, called two-stage hard thresholding with voting. The procedure uses two hard thresholding steps to select strong instruments and to generate candidate sets of valid IVs. Voting takes the candidate...
• Asymptotic properties and information criteria for misspecified generalized linear mixed models AI翻译
  作者：Yu, Dalei; Zhang, Xinyu; Yau, Kelvin K. W.
  作者单位：Yunnan University of Finance & Economics; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; City University of Hong Kong
  摘要：The problem of misspecification poses challenges in model selection. The paper studies the asymptotic properties of estimators for generalized linear mixed models with misspecification under the framework of conditional Kullback-Leibler divergence. A conditional generalized information criterion is introduced, and a model selection procedure is proposed by minimizing the criterion. We prove that the model selection procedure proposed is asymptotically loss efficient when all the candidate mode...
• Sparse generalized eigenvalue problem: optimal statistical rates via truncated Rayleigh flow AI翻译
  作者：Tan, Kean Ming; Wang, Zhaoran; Liu, Han; Zhang, Tong
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Northwestern University
  摘要：The sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large family of high dimensional statistical models, including sparse Fisher's discriminant analysis, canonical correlation analysis and sufficient dimension reduction. The sparse GEP involves solving a non-convex optimization problem. Most existing methods and theory in the context of specific statistical models that are special cases of the sparse GEP require restrictive structural assumptions on the input matrices. We...
• 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...