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作者：Eckley, I. A.; Nason, G. P.
作者单位：Lancaster University; University of Bristol
摘要：Aliasing is often overlooked in time series analysis but can seriously distort the spectrum, the autocovariance and their estimates. We show that dyadic subsampling of a locally stationary wavelet process, which can cause aliasing, results in a process that is the sum of asymptotic white noise and another locally stationary wavelet process with a modified spectrum. We develop a test for the absence of aliasing in a locally stationary wavelet series at a fixed location, and illustrate its appli...
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作者：Fogarty, Colin B.
作者单位：Massachusetts Institute of Technology (MIT)
摘要：In paired randomized experiments, individuals in a given matched pair may differ on prognostically important covariates despite the best efforts of practitioners. We examine the use of regression adjustment to correct for persistent covariate imbalances after randomization, and present two regression-assisted estimators for the sample average treatment effect in paired experiments. Using the potential outcomes framework, we prove that these estimators are consistent for the sample average trea...
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作者：Ertefaie, Ashkan; Strawderman, Robert L.
作者单位：University of Rochester
摘要：Existing methods for estimating optimal dynamic treatment regimes are limited to cases where a utility function is optimized over a fixed time period. We develop an estimation procedure for the optimal dynamic treatment regime over an indefinite time period and derive associated largesample results. The proposed method can be used to estimate the optimal dynamic treatment regime in chronic disease settings. We illustrate this by simulating a dataset corresponding to a cohort of patients with d...
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作者：Forastiere, Laura; Mattei, Alessandra; Ding, Peng
作者单位：University of Florence; University of California System; University of California Berkeley
摘要：In causal mediation analysis, the definitions of the natural direct and indirect effects involve potential outcomes that can never be observed, so-called a priori counterfactuals. This conceptual challenge translates into issues in identification, which requires strong and often unverifiable assumptions, including sequential ignorability. Alternatively, we can deal with post-treatment variables using the principal stratification framework, where causal effects are defined as comparisons of obs...
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作者：Lee, S. M. S.; Wu, Y.
作者单位：University of Hong Kong; University of Waterloo
摘要：We propose a general bootstrap recipe for estimating the distributions of post-model-selection least squares estimators under a linear regression model. The recipe constrains residual bootstrapping within the most parsimonious, approximately correct, models to yield a distribution estimator which is consistent provided any wrong candidate model is sufficiently separated from the approximately correct ones. Our theory applies to a broad class of model selection methods based on information crit...
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作者：Li, Quefeng; Li, Lexin
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Berkeley
摘要：Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achiev...
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作者：Proietti, Tommaso; Giovannelli, Alessandro
作者单位：University of Rome Tor Vergata
摘要：The autocovariance matrix of a stationary random process plays a central role in prediction theory and time series analysis. When the dimension of the matrix is of the same order of magnitude as the number of observations, the sample autocovariance matrix gives an inconsistent estimator. In the nonparametric framework, recent proposals have concentrated on banding and tapering the sample autocovariance matrix. We introduce an alternative approach via a modified Durbin-Levinson algorithm that r...
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作者：Zhang, Xinyu; Chiou, Jeng-Min; Ma, Yanyuan
作者单位：Chinese Academy of Sciences; Academia Sinica - Taiwan; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Prediction is often the primary goal of data analysis. In this work, we propose a novel model averaging approach to the prediction of a functional response variable. We develop a crossvalidation model averaging estimator based on functional linear regression models in which the response and the covariate are both treated as random functions. We show that the weights chosen by the method are asymptotically optimal in the sense that the squared error loss of the predicted function is as small as...
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作者：Picard, Franck; Reynaud-Bouret, Patricia; Roquain, Etienne
作者单位：Centre National de la Recherche Scientifique (CNRS); VetAgro Sup; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite; Universite Paris Cite
摘要：We propose a continuous testing framework to test the intensities of Poisson processes that allows a rigorous definition of the complete testing procedure, from an infinite number of hypotheses to joint error rates. Our work extends procedures based on scanning windows by controlling the familywise error rate and the false discovery rate in a non-asymptotic manner and in a continuous way. We introduce the p-value process on which the decision rule is based. Our method is applied in neuroscienc...
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作者：Basse, Guillaume W.; Airoldi, Edoardo M.
作者单位：Harvard University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：In this paper we consider how to assign treatment in a randomized experiment in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. We use these models to develop restricted randomization strategies for allocating treatment optimally, by minimizing the mean squared error of the estimated average treatment effec...
