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作者：Pensky, Marianna
作者单位：State University System of Florida; University of Central Florida
摘要：In the present paper, we consider a dynamic stochastic network model. The objective is estimation of the tensor of connection probabilities Lambda when it is generated by a Dynamic Stochastic Block Model (DSBM) or a dynamic graphon. In particular, in the context of the DSBM, we derive a penalized least squares estimator (Lambda) over cap of Lambda and show that (Lambda) over cap satisfies an oracle inequality and also attains minimax lower bounds for the risk. We extend those results to estima...
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作者：Spokoiny, Vladimir; Willrich, Niklas
作者单位：Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics; Humboldt University of Berlin; Russian Academy of Sciences; HSE University (National Research University Higher School of Economics)
摘要：The paper focuses on the problem of model selection in linear Gaussian regression with unknown possibly inhomogeneous noise. For a given family of linear estimators {(theta) over tilde (m), m is an element of M}, ordered by their variance, we offer a new smallest accepted approach motivated by Lepski's device and the multiple testing idea. The procedure selects the smallest model which satisfies the acceptance rule based on comparison with all larger models. The method is completely data-drive...
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作者：Chang, Hsin-Wen; McKeague, Ian W.
作者单位：Academia Sinica - Taiwan; Columbia University
摘要：New nonparametric tests for the ordering of multiple survival functions are developed with the possibility of right censorship taken into account. The motivation comes from noninferiority trials with multiple treatments. The proposed tests are based on nonparametric likelihood ratio statistics, which are known to provide more powerful tests than Wald-type procedures, but in this setting have only been studied for pairs of survival functions or in the absence of censoring. We introduce a novel ...
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作者：Gronneberg, Steffen; Holcblat, Benjamin
作者单位：BI Norwegian Business School; University of Luxembourg
摘要：We establish general and versatile results regarding the limit behavior of the partial-sum process of ARMAX residuals. Illustrations include ARMA with seasonal dummies, misspecified ARMAX models with autocorrelated errors, nonlinear ARMAX models, ARMA with a structural break, a wide range of ARMAX models with infinite-variance errors, weak GARCH models and the consistency of kernel estimation of the density of ARMAX errors. Our results identify the limit distributions, and provide a general al...
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作者：Li, Zeng; Lam, Clifford; Yao, Jianfeng; Yao, Qiwei
作者单位：University of London; London School Economics & Political Science; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of London; London School Economics & Political Science; University of Hong Kong
摘要：Testing for white noise is a classical yet important problem in statistics, especially for diagnostic checks in time series modeling and linear regression. For high-dimensional time series in the sense that the dimension p is large in relation to the sample size T, the popular omnibus tests including the multivariate Hosking and Li-McLeod tests are extremely conservative, leading to substantial power loss. To develop more relevant tests for high-dimensional cases, we propose a portmanteau-type...
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作者：Chen, Wei-Kuo
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：We consider the problem of detecting a deformation from a symmetric Gaussian random p-tensor (p >= 3) with a rank-one spike sampled from the Rademacher prior. Recently, in Lesieur et al. (Barbier, Krzakala, Macris, Miolane and Zdeborova (2017)), it was proved that there exists a critical threshold beta(p) so that when the signal-to-noise ratio exceeds beta(p), one can distinguish the spiked and unspiked tensors and weakly recover the prior via the minimal mean-square-error method. On the other...
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作者：Chu, Lynna; Chen, Hao
作者单位：University of California System; University of California Davis
摘要：We consider the testing and estimation of change-points, locations where the distribution abruptly changes, in a sequence of multivariate or non-Euclidean observations. We study a nonparametric framework that utilizes similarity information among observations, which can be applied to various data types as long as an informative similarity measure on the sample space can be defined. The existing approach along this line has low power and/or biased estimates for change-points under some common s...
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作者：Cai, T. Tony; Ma, Jing; Zhang, Linjun
作者单位：University of Pennsylvania
摘要：Unsupervised learning is an important problem in statistics and machine learning with a wide range of applications. In this paper, we study clustering of high-dimensional Gaussian mixtures and propose a procedure, called CHIME, that is based on the EM algorithm and a direct estimation method for the sparse discriminant vector. Both theoretical and numerical properties of CHIME are investigated. We establish the optimal rate of convergence for the excess misclustering error and show that CHIME ...
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作者：Ma, Shujie; Zhu, Liping; Zhang, Zhiwei; Tsai, Chih-Ling; Carroll, Raymond J.
作者单位：University of California System; University of California Riverside; Renmin University of China; University of California System; University of California Davis; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
摘要：A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most exi...
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作者：Hu, Jiang; Li, Weiming; Liu, Zhi; Zhou, Wang
作者单位：Northeast Normal University - China; Northeast Normal University - China; Shanghai University of Finance & Economics; University of Macau; National University of Singapore
摘要：This paper discusses fluctuations of linear spectral statistics of high-dimensional sample covariance matrices when the underlying population follows an elliptical distribution. Such population often possesses high order correlations among their coordinates, which have great impact on the asymptotic behaviors of linear spectral statistics. Taking such kind of dependency into consideration, we establish a new central limit theorem for the linear spectral statistics in this paper for a class of ...
