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作者：Armillotta, Mirko; Fokianos, Konstantinos
作者单位：Vrije Universiteit Amsterdam; University of Cyprus
摘要：We study general nonlinear models for time series networks of integer and continuous-valued data. The vector of high-dimensional responses, measured on the nodes of a known network, is regressed nonlinearly on its lagged value and on lagged values of the neighboring nodes by employing a smooth link function. We study stability conditions for such multivariate process and develop quasi-maximum likelihood inference when the network dimension is increasing. In addition, we study linearity score t...
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作者：Baek, Changryong; Duker, Marie-christine; Pipiras, Vladas
作者单位：Sungkyunkwan University (SKKU); Cornell University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：This work develops nonasymptotic theory for estimation of the longrun variance matrix and its inverse, the so-called precision matrix, for highdimensional time series under general assumptions on the dependence structure including long-range dependence. The estimation involves shrinkage techniques, which are thresholding and penalizing versions of the classical multivariate local Whittle estimator. The results ensure consistent estimation in a double asymptotic regime where the number of compo...
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作者：Reiss, Markus; Winkelmann, Lars
作者单位：Humboldt University of Berlin; Free University of Berlin
摘要：We study the rank of the instantaneous or spot covariance matrix ⠂X (t) of a multidimensional process X (t). Given high-frequency observations X(i/n), i = 0, ... , n, we test the null hypothesis rank(⠂X(t)) & LE; r for all t against local alternatives where the average (r + 1)st eigenvalue is larger than some signal detection rate vn. A major problem is that the inherent averaging in local covariance statistics produces a bias that distorts the rank statistics. We show that the bias depends ...
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作者：Donoho, David; Gavish, Matan; Romanov, Elad
作者单位：Stanford University; Hebrew University of Jerusalem
摘要：We derive a formula for optimal hard thresholding of the singular value decomposition in the presence of correlated additive noise; although it nomi-nally involves unobservables, we show how to apply it even where the noise covariance structure is not a priori known or is not independently estimable. The proposed method, which we call ScreeNOT, is a mathematically solid alternative to Cattell's ever-popular but vague scree plot heuristic from 1966. ScreeNOT has a surprising oracle property: it...
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作者：Huang, Tzu-jung; Luedtke, Alex; Mckeague, Ian w.
作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle; Columbia University
摘要：This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable in high dimensions. Machine learning tools are commonly used to provide predictions of survival outcomes, but the estimated effect of a selected predictor suffers from confirmation b...
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作者：Klopp, Olga; Panov, Maxim; Sigalla, Suzanne; Tsybakov, Alexandre B.
作者单位：ESSEC Business School; Institut Polytechnique de Paris; ENSAE Paris
摘要：Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering documents from big unstructured corpora into topics is an important task in various fields, such as image analysis, e-commerce, social networks and population genetics. Since the number of topics is typically substantially smaller than the size of the corpus and of the dictionary, the methods of topic modeling can lead ...
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作者：Spencer, Neil A.; Shalizi, Cosma Rohilla
作者单位：University of Connecticut; Carnegie Mellon University
摘要：When modeling network data using a latent position model, it is typical to assume that the nodes' positions are independently and identically distributed. However, this assumption implies the average node degree grows linearly with the number of nodes, which is inappropriate when the graph is thought to be sparse. We propose an alternative assumption-that the latent positions are generated according to a Poisson point process-and show that it is compatible with various levels of sparsity. Unli...
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作者：Chen, Song xi; Qiu, Yumou; Zhang, Shuyi
作者单位：Peking University; Peking University; Peking University; East China Normal University
摘要：This paper considers one-sample testing of a high-dimensional covariance matrix by deriving the detection boundary as a function of the signal sparsity and signal strength under the sparse alternative hypotheses. It first shows that the optimal detection boundary for testing sparse means is the minimax detection lower boundary for testing the covariance matrix. A multilevel thresholding test is proposed and is shown to be able to attain the detection lower boundary over a substantial range of ...
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作者：Roycraft, Benjamin; Krebs, Johannes; Polonik, Wolfgang
作者单位：University of California System; University of California Davis
摘要：We investigate multivariate bootstrap procedures for general stabilizing statistics, with specific application to topological data analysis. The work relates to other general results in the area of stabilizing statistics, including central limit theorems for geometric and topological functionals of Poisson and binomial processes in the critical regime, where limit theorems prove difficult to use in practice, motivating the use of a bootstrap approach. A smoothed bootstrap procedure is shown to...
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作者：Chernozhukov, Victor; Hansen, Christian; Liao, Yuan; Zhu, Yinchu
作者单位：Massachusetts Institute of Technology (MIT); University of Chicago; Rutgers University System; Rutgers University New Brunswick; Brandeis University
摘要：This paper studies inference in linear models with a high-dimensional parameter matrix that can be well approximated by a spiked low-rank matrix. A spiked low-rank matrix has rank that grows slowly compared to its dimensions and nonzero singular values that diverge to infinity. We show that this framework covers a broad class of models of latent variables, which can accommodate matrix completion problems, factor models, varying coefficient models and heterogeneous treatment effects. For infere...
