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作者：Li, Zeng; Wang, Qinwen; Yao, Jianfeng
作者单位：University of Hong Kong
摘要：Identifying the number of factors in a high-dimensional factor model has attracted much attention in recent years and a general solution to the problem is still lacking. A promising ratio estimator based on singular values of lagged sample auto-covariance matrices has been recently proposed in the literature with a reasonably good performance under some specific assumption on the strength of the factors. Inspired by this ratio estimator and as a first main contribution, this paper proposes a c...
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作者：Chambaz, Antoine; Zheng, Wenjing; van der Laan, Mark J.
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：This article studies the targeted sequential inference of an optimal treatment rule (TR) and its mean reward in the nonexceptional case, that is, assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption. Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle, actually infers the mean reward under the current estimate of the optimal TR. This data-adapti...
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作者：Metelkina, Asya; Pronzato, Luc
作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Cote d'Azur; Universite Cote d'Azur; Centre National de la Recherche Scientifique (CNRS)
摘要：Covariate-adaptive treatment allocation is considered in the situation when a compromise must be made between information (about the dependency of the probability of success of each treatment upon influential covariates) and cost (in terms of number of subjects receiving the poorest treatment). Information is measured through a design criterion for parameter estimation, the cost is additive and is related to the success probabilities. Within the framework of approximate design theory, the dete...
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作者：Su, Weijie; Bogdan, Malgorzata; Candes, Emmanuel
作者单位：University of Pennsylvania; University of Wroclaw; Stanford University; Stanford University
摘要：In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any. This paper shows that in a regime of linear sparsity-meaning that the fraction of variables with a nonvanishing effect tends to a constant, however small-this cannot really be the case, even when the design variables are stochastically independent. We demonstrate that true featur...
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作者：Todorov, Viktor
作者单位：Northwestern University
摘要：In this paper, we propose a test for deciding whether the jump activity index of a discretely observed It semimartingale of pure-jump type (i.e., one without a diffusion) varies over a fixed interval of time. The asymptotic setting is based on observations within a fixed time interval with mesh of the observation grid shrinking to zero. The test is derived for semimartingales whose spot jump compensator around zero is like that of a stable process, but importantly the stability index can vary...
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作者：Jiang, Jiming; Li, Cong; Paul, Debashis; Yang, Can; Zhao, Hongyu
作者单位：University of California System; University of California Davis; Takeda Pharmaceutical Company Ltd; Takeda Pharmaceuticals International, Inc.; Hong Kong Baptist University; Yale University
摘要：We study behavior of the restricted maximum likelihood (REML) estimator under a misspecified linear mixed model (LMM) that has received much attention in recent genome-wide association studies. The asymptotic analysis establishes consistency of the REML estimator of the variance of the errors in the LMM, and convergence in probability of the REML estimator of the variance of the random effects in the LMM to a certain limit, which is equal to the true variance of the random effects multiplied b...
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作者：Carlier, Guillaume; Chernozhukov, Victor; Galichon, Alfred
作者单位：Universite PSL; Universite Paris-Dauphine; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); New York University; New York University
摘要：We propose a notion of conditional vector quantile function and a vector quantile regression. A conditional vector quantile function (CVQF) of a random vector Y, taking values in R-d given covariates Z = z, taking values in R-k, is a map u bar right arrow Q(Y vertical bar Z) (u, z), which is monotone, in the sense of being a gradient of a convex function, and such that given that vector U follows a reference non-atomic distribution F-U, for instance uniform distribution on a unit cube in Rd, t...
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作者：Lee, Jason D.; Sun, Dennis L.; Sun, Yuekai; Taylor, Jonathan E.
作者单位：University of California System; University of California Berkeley; California State University System; California Polytechnic State University San Luis Obispo; University of California System; University of California Berkeley; Stanford University
摘要：We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes. the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.
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作者：Qiao, Wanli; Polonik, Wolfgang
作者单位：University of California System; University of California Davis
摘要：This paper provides a rigorous study of the nonparametric estimation of filaments or ridge lines of a probability density f. Points on the filament are considered as local extrema of the density when traversing the support of f along the integral curve driven by the vector field of second eigenvectors of the Hessian of f. We parametrize points on the filaments by such integral curves, and thus both the estimation of integral curves and of filaments will be considered via a plug-in method using...
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作者：Su, Weijie; Candes, Emmanuel
作者单位：Stanford University; Stanford University
摘要：We consider high-dimensional sparse regression problems in which we observe y = X beta + z, where X is an n x p design matrix and z is an n dimensional vector of independent Gaussian errors, each with variance sigma(2). Our focus is on the recently introduced SLOPE estimator [Ann. Appi. Stat. 9 (2015) 1103-1140], which regularizes the least-squares estimates with the rank-dependent penalty Sigma(1 <= i <= p) lambda(i)vertical bar(beta) over cap vertical bar((i)), where vertical bar(beta) over ...
