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作者：Cui, Y.; Tchetgen, E. J. Tchetgen
作者单位：Zhejiang University; Zhejiang University; University of Pennsylvania
摘要：While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a finite-dimensional functional defined on a semiparametric model, when the latter admits a doubly robust estimating function and several candidate machine learning algorithms are avai...
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作者：Cohen, P. L.; Fogarty, C. B.
作者单位：Massachusetts Institute of Technology (MIT); University of Michigan System; University of Michigan
摘要：In randomized experiments, adjusting for observed features when estimating treatment effects has been proposed as a way to improve asymptotic efficiency. However, among parametric methods, only linear regression has been proven to form an estimate of the average treatment effect that is asymptotically no less efficient than the treated-minus-control difference in means regardless of the true data generating process. Randomized treatment assignment provides this do-no-harm property, with neithe...
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作者：Qu, Lianqiang; Sun, Liuquan; Sun, Yanqing
作者单位：Central China Normal University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of North Carolina; University of North Carolina Charlotte
摘要：Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is an extension of cause of failure in a classical competing risk model where cause of failure is replaced by a continuous mark only observed at uncensored failure times. An example of the continuous mark variable is the genetic distance that measures dissimilarity between the infecting virus and the ...
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作者：Mccloskey, A.
作者单位：University of Colorado System; University of Colorado Boulder
摘要：I propose a new type of confidence interval for correct asymptotic inference after using data to select a model of interest without assuming any model is correctly specified. This hybrid confidence interval is constructed by combining techniques from the selective inference and post-selection inference literatures to yield a short confidence interval across a wide range of data realizations. I show that hybrid confidence intervals have correct asymptotic coverage, uniformly over a large class ...
