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作者：Zhao, Anqi; Ding, Peng; Mukerjee, Rahul; Dasgupta, Tirthankar
作者单位：Harvard University; University of California System; University of California Berkeley; Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; Rutgers University System; Rutgers University New Brunswick
摘要：Under the potential outcomes framework, we propose a randomization based estimation procedure for causal inference from split-plot designs, with special emphasis on 2(2) designs that naturally arise in many social, behavioral and biomedical experiments. Point estimators of factorial effects are obtained and their sampling variances are derived in closed form as linear combinations of the between- and within-group covariances of the potential outcomes. Results are compared to those under comple...
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作者：Escanciano, Juan Carlos; Carlos Pardo-Fernandez, Juan; Van Keilegom, Ingrid
作者单位：Indiana University System; Indiana University Bloomington; Universidade de Vigo; KU Leuven
摘要：This article proposes a new general methodology for constructing nonparametric and semiparametric Asymptotically Distribution-Free (ADF) tests for semiparametric hypotheses in regression models for possibly dependent data coming from a strictly stationary process. Classical tests based on the difference between the estimated distributions of the restricted and unrestricted regression errors are not ADF. In this article, we introduce a novel transformation of this difference that leads to ADF t...
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作者：Chen, Xi; Liu, Weidong
作者单位：New York University; Shanghai Jiao Tong University; Shanghai Jiao Tong University
摘要：Testing independence among a number of (ultra) high-dimensional random samples is a fundamental and challenging problem. By arranging n identically distributed p-dimensional random vectors into a p x n data matrix, we investigate the problem of testing independence among columns under the matrix-variate normal modeling of data. We propose a computationally simple and tuning-free test statistic, characterize its limiting null distribution, analyze the statistical power and prove its minimax opt...
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作者：Lv, Shaogao; Lin, Huazhen; Lian, Heng; Huang, Jian
作者单位：Nanjing Audit University; Southwestern University of Finance & Economics - China; City University of Hong Kong; University of Iowa
摘要：This paper considers the estimation of the sparse additive quantile regression (SAQR) in high-dimensional settings. Given the nonsmooth nature of the quantile loss function and the nonparametric complexities of the component function estimation, it is challenging to analyze the theoretical properties of ultrahigh-dimensional SAQR. We propose a regularized learning approach with a two-fold Lasso-type regularization in a reproducing kernel Hilbert space (RKHS) for SAQR. We establish nonasymptoti...
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作者：Pretorius, Charl; Swanepoel, Jan W. H.
作者单位：North West University - South Africa
摘要：We propose a new method, based on sample splitting, for constructing bootstrap confidence bounds for a parameter appearing in the regular smooth function model. It has been demonstrated in the literature, for example, by Hall [Ann. Statist. 16 (1988) 927-985; The Bootstrap and Edgeworth Expansion (1992) Springer], that the well-known percentile-t method for constructing bootstrap confidence bounds typically incurs a coverage error of order O(n(-1)), with n being the sample size. Our version of...
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作者：Jankova, Jana; van de Geer, Sara
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：Asymptotic lower bounds for estimation play a fundamental role in assessing the quality of statistical procedures. In this paper, we propose a framework for obtaining semiparametric efficiency bounds for sparse high-dimensional models, where the dimension of the parameter is larger than the sample size. We adopt a semiparametric point of view: we concentrate on one-dimensional functions of a high-dimensional parameter. We follow two different approaches to reach the lower bounds: asymptotic Cr...
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作者：Rockova, Veronika
作者单位：University of Chicago
摘要：We introduce a new framework for estimation of sparse normal means, bridging the gap between popular frequentist strategies (LASSO) and popular Bayesian strategies (spike-and-slab). The main thrust of this paper is to introduce the family of Spike-and-Slab LASSO (SS-LASSO) priors, which form a continuum between the Laplace prior and the point-mass spike-and-slab prior. We establish several appealing frequentist properties of SS-LASSO priors, contrasting them with these two limiting cases. Firs...
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作者：Jiang, Bai; Wu, Tung-Yu; Jin, Yifan; Wong, Wing H.
作者单位：Stanford University; Stanford University
摘要：The Contrastive Divergence ( CD) algorithm has achieved notable success in training energy-based models including Restricted Boltzmann Machines and played a key role in the emergence of deep learning. The idea of this algorithm is to approximate the intractable term in the exact gradient of the log-likelihood function by using short Markov chain Monte Carlo (MCMC) runs. The approximate gradient is computationally-cheap but biased. Whether and why the CD algorithm provides an asymptotically con...
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作者：Gloter, Arnaud; Loukianova, Dasha; Mai, Hilmar
作者单位：Universite Paris Saclay; Institut Polytechnique de Paris; ENSAE Paris
摘要：The problem of drift estimation for the solution X of a stochastic differential equation with Levy-type jumps is considered under discrete high-frequency observations with a growing observation window. An efficient and asymptotically normal estimator for the drift parameter is constructed under minimal conditions on the jump behavior and the sampling scheme. In the case of a bounded jump measure density, these conditions reduce to n Delta(3-epsilon)(n)-> 0, where n is the number of observation...
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作者：Strauch, Claudia
作者单位：University of Mannheim
摘要：Consider some multivariate diffusion process X = (X-t)(t >= 0) with unique invariant probability measure and associated invariant density rho, and assume that a continuous record of observations X-T = (X-t)(0 <= t <= T) of X is available. Recent results on functional inequalities for symmetric Markov semi groups are used in the statistical analysis of kernel estimators (rho) over cap (T) = (rho) over cap (T) (X-T) of rho. For the basic problem of estimation with respect to sup-norm risk under ...
