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作者：Wang, Guanghui; Zou, Changliang; Yin, Guosheng
作者单位：Nankai University; Nankai University; University of Hong Kong
摘要：We consider a sequence of multinomial data for which the probabilities associated with the categories are subject to abrupt changes of unknown magnitudes at unknown locations. When the number of categories is comparable to or even larger than the number of subjects allocated to these categories, conventional methods such as the classical Pearson's chi-squared test and the deviance test may not work well. Motivated by high-dimensional homogeneity tests, we propose a novel change-point detection...
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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：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：Brouste, Alexandre; Fukasawa, Masaaki
作者单位：Le Mans Universite; University of Osaka
摘要：Local Asymptotic Normality (LAN) property for fractional Gaussian noise under high-frequency observations is proved with nondiagonal rate matrices depending on the parameter to be estimated. In contrast to the LAN families in the literature, nondiagonal rate matrices are inevitable. As consequences of the LAN property, a maximum likelihood sequence of estimators is shown to be asymptotically efficient and the likelihood ratio test on the Hurst parameter is shown to be an asymptotically uniform...
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作者：Zhang, Bo; Pan, Guangming; Gao, Jiti
作者单位：Nanyang Technological University; Monash University
摘要：Let {Z(ij)} be independent and identically distributed (i.i.d.) random variables with EZ(ij) = 0, E vertical bar Z(ij)vertical bar(2) = 1 and E vertical bar Z(ij)vertical bar(4) < infinity. Define linear processes Y-tj = Sigma(infinity)(k=0) b(k) Z(t -k,j) with Sigma(infinity)(i=0) vertical bar b(i)vertical bar < infinity. Consider a p-dimensional time series model of the form x(t) = Pi x(t-1) + Sigma(1/2)y(t), 1 <= t <= T with y(t) = (Y-t(1), ..., Y-tp)' and Sigma(1/2) be the square root of a...
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作者：Tang, Minh; Priebe, Carey E.
作者单位：Johns Hopkins University
摘要：We prove a central limit theorem for the components of the eigenvectors corresponding to the d largest eigenvalues of the normalized Laplacian matrix of a finite dimensional random dot product graph. As a corollary, we show that for stochastic blockmodel graphs, the rows of the spectral embedding of the normalized Laplacian converge to multivariate normals and, furthermore, the mean and the covariance matrix of each row are functions of the associated vertex's block membership. Together with p...
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作者：Zhou, Wen-Xin; Bose, Koushiki; Fan, Jianqing; Liu, Han
作者单位：University of California System; University of California San Diego; Princeton University; Fudan University
摘要：Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber in 1973 [Ann. Statist. 1 (1973) 799-821], robust alternatives to the method of least squares are sorely needed. To achieve robustness against heavy-tailed sampling distributions, we revisit the Huber estimator from a new perspective by letting the tuning parameter involved diverge with the sample size. In this paper, ...
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作者：Koul, Hira L.; Song, Weixing; Zhu, Xiaoqing
作者单位：Michigan State University; Kansas State University
摘要：This paper investigates a class of goodness-of-fit tests for fitting an error density in linear regression models with measurement error in covariates. Each test statistic is the integrated square difference between the deconvolution kernel density estimator of the regression model error density and a smoothed version of the null error density, an analog of the so-called Bickel and Rosenblatt test statistic. The asymptotic null distributions of the proposed test statistics are derived for both...
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作者：Chang, Jinyuan; Guo, Bin; Yao, Qiwei
作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of London; London School Economics & Political Science
摘要：We extend the principal component analysis (PCA) to second-order stationary vector time series in the sense that we seek for a contemporaneous linear transformation for a p-variate time series such that the transformed series is segmented into several lower-dimensional subseries, and those subseries are uncorrelated with each other both contemporaneously and serially. Therefore, those lower-dimensional series can be analyzed separately as far as the linear dynamic structure is concerned. Techn...
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作者：Dalalyan, Arnak S.; Grappin, Edwin; Paris, Quentin
作者单位：Institut Polytechnique de Paris; ENSAE Paris; Universite Paris Saclay; HSE University (National Research University Higher School of Economics)
摘要：In this paper, we study the statistical behaviour of the Exponentially Weighted Aggregate (EWA) in the problem of high-dimensional regression with fixed design. Under the assumption that the underlying regression vector is sparse, it is reasonable to use the Laplace distribution as a prior. The resulting estimator and, specifically, a particular instance of it referred to as the Bayesian lasso, was already used in the statistical literature because of its computational convenience, even though...
