-
作者：Su, Weijie J.
作者单位：University of Pennsylvania
摘要：Applied statisticians use sequential regression procedures to rank explanatory variables and, in settings of low correlations between variables and strong true effect sizes, expect that variables at the top of this ranking are truly relevant to the response. In a regime of certain sparsity levels, however, we show that the lasso, forward stepwise regression, and least angle regression include the first spurious variable unexpectedly early. We derive a sharp prediction of the rank of the first ...
-
作者：Avella-Medina, Marco; Battey, Heather S.; Fan, Jianqing; Li, Quefeng
作者单位：Massachusetts Institute of Technology (MIT); Imperial College London; Princeton University; University of North Carolina; University of North Carolina Chapel Hill
摘要：High-dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components. Covariance and precision matrices provide a useful summary of such structure, yet the performance of popular matrix estimators typically hinges upon a sub-Gaussianity assumption. This paper presents robust matrix estimators whose performance is guaranteed for a much richer class of distributions. The proposed estimators, under a bounded fourth moment a...
-
作者：Claeskens, G.; Krivobokova, T.; Opsomer, J. D.
-
作者：Tang, Yanlin; Wang, Huixia Judy; Barut, Emre
作者单位：Tongji University; George Washington University
摘要：Researchers sometimes have a priori information on the relative importance of predictors that can be used to screen out covariates. An important question is whether any of the discarded covariates have predictive power when the most relevant predictors are included in the model. We consider testing whether any discarded covariate is significant conditional on some pre-chosen covariates. We propose a maximum-type test statistic and show that it has a nonstandard asymptotic distribution, giving ...
-
作者：Yiu, Sean; Su, Li
作者单位：MRC Biostatistics Unit; University of Cambridge
摘要：Weighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions for weight estimat...
-
作者：Ding, Peng; Dasgupta, Tirthankar
作者单位：University of California System; University of California Berkeley; Rutgers University System; Rutgers University New Brunswick
摘要：Fisher randomization tests for Neyman's null hypothesis of no average treatment effect are considered in a finite-population setting associated with completely randomized experiments involving more than two treatments. The consequences of using the F statistic to conduct such a test are examined, and we argue that under treatment effect heterogeneity, use of the F statistic in the Fisher randomization test can severely inflate the Type I error under Neyman's null hypothesis. We propose to use ...
-
作者：Dette, H.; Guchenko, R.; Melas, V. B.; Wong, W. K.
作者单位：Ruhr University Bochum; Saint Petersburg State University; University of California System; University of California Los Angeles
摘要：Much work on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters. Recent work allows errors in the models to be nonnormally distributed but still requires the specification of the mean structures. Otsu (2008) proposed optimal discriminating designs for semiparametric models by generalizing the Kullback-Leibler optimality criterion proposed by Lpez-Fidalgo et al. (2007). This paper develops a relatively simple strategy for findin...
-
作者：Stallard, Nigel; Kimani, Peter K.
作者单位：University of Warwick
摘要：Multi-arm multi-stage clinical trials compare several experimental treatments with a control treatment, with poorly performing treatments dropped at interim analyses. This leads to inferential challenges, including the construction of unbiased treatment effect estimators. A number of estimators which are unbiased conditional on treatment selection have been proposed, but are specific to certain selection rules, may ignore the comparison to the control and are not all minimum variance. We obtai...
-
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...
-
作者：Matias, C.; Rebafka, T.; Villers, F.
作者单位：Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite
摘要：We propose an extension of the stochastic block model for recurrent interaction events in continuous time, where every individual belongs to a latent group and conditional interactions between two individuals follow an inhomogeneous Poisson process with intensity driven by the individuals' latent groups. We show that the model is identifiable and estimate it with a semiparametric variational expectation-maximization algorithm. We develop two versions of the method, one using a nonparametric hi...
