6期

最新文章

• CHANGE-POINT ANALYSIS WITH IRREGULAR SIGNALS AI翻译
  作者：Kley, Tobias; Liu, Yuhan philip; Cao, Hongyuan; Wu, Wei biao
  作者单位：University of Gottingen; University of Chicago; State University System of Florida; Florida State University
  摘要：This paper considers the problem of testing and estimation of change point where signals after the change point can be highly irregular, which departs from the existing literature that assumes signals after the change point to be piecewise constant or vary smoothly. A two-step approach is proposed to effectively estimate the location of the change point. The first step consists of a preliminary estimation of the change point that allows us to obtain unknown parameters for the second step. In t...
• ESTIMATION OF THE SPECTRAL MEASURE FROM CONVEX COMBINATIONS OF REGULARLY VARYING RANDOM VECTORS AI翻译
  作者：Oesting, Marco; Wintenberger, Olivier
  作者单位：University of Stuttgart; University of Stuttgart; Universite Paris Cite; Sorbonne Universite
  摘要：The extremal dependence structure of a regularly varying random vector X is fully described by its limiting spectral measure. In this paper, we investigate how to recover characteristics of the measure, such as extremal coefficients, from the extremal behaviour of convex combinations of components of X. Our considerations result in a class of new estimators of moments of the corresponding combinations for the spectral vector. We show asymptotic normality by means of a functional limit theorem ...
• STATISTICAL INFERENCE FOR FOUR-REGIME SEGMENTED REGRESSION MODELS AI翻译
  作者：Yan, Han; Chen, Song Xi
  作者单位：Peking University; Tsinghua University
  摘要：Segmented regression models offer model flexibility and interpretability as compared to the global parametric and the nonparametric models, and yet are challenging in both estimation and inference. We consider a four-regime segmented model for temporally dependent data with segmenting boundaries depending on multivariate covariates with nondiminishing boundary effects. A mixed integer quadratic programming algorithm is formulated to facilitate the least square estimation of the regression and ...
• TENSOR-ON-TENSOR REGRESSION: RIEMANNIAN OPTIMIZATION, OVER-PARAMETERIZATION, STATISTICAL-COMPUTATIONAL GAP AND THEIR INTERPLAY AI翻译
  作者：Luo, Yuetian; Zhang, Anru r.
  作者单位：University of Chicago; Duke University; Duke University
  摘要：We study the tensor-on-tensor regression, where the goal is to connect tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without prior knowledge of its intrinsic rank. We propose the Riemethods and cope with the challenge of unknown rank by studying the effect of rank over-parameterization. We provide the first convergence guarantee for the general tensor-on-tensor regression by showing that RGD and RGN respectively converge linearly and quadratically to a st...
• INCREASING DIMENSION ASYMPTOTICS FOR TWO-WAY CROSSED MIXED EFFECT MODELS AI翻译
  作者：Lyu, Ziyang; Sisson, S. A.; Welsh, A. H.
  作者单位：University of New South Wales Sydney; University of New South Wales Sydney; Australian National University
  摘要：This paper presents asymptotic results for the maximum likelihood and restricted maximum likelihood (REML) estimators within a two-way crossed mixed effect model, when the number of rows, columns, and the number of observations per cell tend to infinity. The relative growth rate for the number of rows, columns, and cells is unrestricted, whether considered pairwise or collectively. Under very mild conditions (which include moment conditions instead of requiring normality for either the random ...
• DEEP NEURAL NETWORKS FOR NONPARAMETRIC INTERACTION MODELS WITH DIVERGING DIMENSION AI翻译
  作者：Bhattacharya, Sohom; Fan, Jianqing; Mukherjee, Debarghya
  作者单位：State University System of Florida; University of Florida; Princeton University; Boston University
  摘要：Deep neural networks have achieved tremendous success due to their representation power and adaptation to low-dimensional structures. Their potential for estimating structured regression functions has been recently established in the literature. However, most of the studies require the input dimension to be fixed, and consequently, they ignore the effect of dimension on the rate of convergence and hamper their applications to modern big data with high dimensionality. In this paper, we bridge t...
• SKEWED BERNSTEIN-VON MISES THEOREM AND SKEW-MODAL APPROXIMATIONS AI翻译
  作者：Durante, Daniele; Pozza, Francesco; Szabo, Botond
  作者单位：Bocconi University; Bocconi University
  摘要：Gaussian deterministic approximations are routinely employed in Bayesian statistics to ease inference when the target posterior is intractable. While these approximations are justified, in asymptotic regimes, by Bernstein-von Mises type results, in practice the expected Gaussian behavior might poorly represent the actual shape of the target posterior, thus affecting approximation accuracy. Motivated by these considerations, we derive an improved class of closed-form and valid deterministic app...
• TESTING NETWORK CORRELATION EFFICIENTLY VIA COUNTING TREES AI翻译
  作者：Mao, Cheng; Wu, Yihong; Xu, Jiaming; Yu, Sophie h
  作者单位：University System of Georgia; Georgia Institute of Technology; Yale University; Duke University; University of Pennsylvania
  摘要：We propose a new procedure for testing whether two networks are edgecorrelated through some latent vertex correspondence. The test statistic is based on counting the cooccurrences of signed trees for a family of nonisomorphic trees. When the two networks are Erdos-R & eacute;nyi random graphs G(n, q) that are either independent or correlated with correlation coefficient rho, our test runs in n(2+o(1)) time and succeeds with high probability as n -> infinity, provided that n min{q, 1 - q} >= n(...
• ON THE STATISTICAL COMPLEXITY OF SAMPLE AMPLIFICATION AI翻译
  作者：Axelrod, Brian; Garg, Shivam; Han, Yanjun; Sharan, Vatsal; Valiant, Gregory
  作者单位：Stanford University; Microsoft; New York University; New York University; University of Southern California
  摘要：The sample amplification problem formalizes the following question: Given n i.i.d. samples drawn from an unknown distribution P, when is it possible to produce a larger set of n + m samples which cannot be distinguished from n + m i.i.d. samples drawn from P? In this work, we provide a firm statistical foundation for this problem by deriving generally applicable amplification procedures, lower bound techniques and connections to existing statistical notions. Our techniques apply to a large cla...
• NOISY RECOVERY FROM RANDOM LINEAR OBSERVATIONS: SHARP MINIMAX RATES UNDER ELLIPTICAL CONSTRAINTS AI翻译
  作者：Pathak, Reese; Wainwright, Martin J.; Xiao, Lin
  作者单位：University of California System; University of California Berkeley; Massachusetts Institute of Technology (MIT)
  摘要：Estimation problems with constrained parameter spaces arise in various settings. In many of these problems, the observations available to the statistician can be modelled as arising from the noisy realization of the image of a random linear operator; an important special case is random design regression. We derive sharp rates of estimation for arbitrary compact elliptical parameter sets and demonstrate how they depend on the distribution of the random linear operator. Our main result is a func...