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作者：Wang, Nanwei; Rauh, Johannes; Massam, Helene
作者单位：York University - Canada; York University - Canada; Max Planck Society
摘要：The existence of the maximum likelihood estimate in a hierarchical log-linear model is crucial to the reliability of inference for this model. Determining whether the estimate exists is equivalent to finding whether the sufficient statistics vector t belongs to the boundary of the marginal polytope of the model. The dimension of the smallest face F-t containing t determines the dimension of the reduced model which should be considered for correct inference. For higher-dimensional problems, it ...
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作者：Hsu, Hsiang-Ling; Ing, Ching-Kang; Tong, Howell
作者单位：National University Kaohsiung; National Tsing Hua University; University of Electronic Science & Technology of China; University of London; London School Economics & Political Science
摘要：Consider finite parametric time series models. I have n observations and k models, which model should I choose on the basis of the data alone is a frequently asked question in many practical situations. This poses the key problem of selecting a model from a collection of candidate models, none of which is necessarily the true data generating process (DGP). Although existing literature on model selection is vast, there is a serious lacuna in that the above problem does not seem to have received...
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作者：Qin, Likuan; Todorov, Viktor
作者单位：Northwestern University; Northwestern University
摘要：This paper develops a nonparametric estimator for the Levy density of an asset price, following an Ito semimartingale, implied by short-maturity options. The asymptotic setup is one in which the time to maturity of the available options decreases, the mesh of the available strike grid shrinks and the strike range expands. The estimation is based on aggregating the observed option data into nonparametric estimates of the conditional characteristic function of the return distribution, the deriva...
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作者：Chen, Hao
作者单位：University of California System; University of California Davis
摘要：We propose a new framework for the detection of change-points in online, sequential data analysis. The approach utilizes nearest neighbor information and can be applied to sequences of multivariate observations or non-Euclidean data objects, such as network data. Different stopping rules are explored, and one specific rule is recommended due to its desirable properties. An accurate analytic approximation of the average run length is derived for the recommended rule, making it an easy off-the-s...
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作者：Hung, Kenneth; Fithian, William
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：Many statistical experiments involve comparing multiple population groups. For example, a public opinion poll may ask which of several political candidates commands the most support; a social scientific survey may report the most common of several responses to a question; or, a clinical trial may compare binary patient outcomes under several treatment conditions to determine the most effective treatment. Having observed the winner (largest observed response) in a noisy experiment, it is natura...
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作者：Qin, Qian; Hobert, James P.
作者单位：State University System of Florida; University of Florida
摘要：The use of MCMC algorithms in high dimensional Bayesian problems has become routine. This has spurred so-called convergence complexity analysis, the goal of which is to ascertain how the convergence rate of a Monte Carlo Markov chain scales with sample size, n, and/or number of covariates, p. This article provides a thorough convergence complexity analysis of Albert and Chib's [J. Amer. Statist. Assoc. 88 (1993) 669-679] data augmentation algorithm for the Bayesian probit regression model. The...
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作者：Bevilacqua, Moreno; Faouzi, Tarik; Furrer, Reinhard; Porcu, Emilio
作者单位：Universidad de Valparaiso; Universidad del Bio-Bio; University of Zurich; University of Zurich; Newcastle University - UK; Universidad de Atacama
摘要：We study estimation and prediction of Gaussian random fields with covariance models belonging to the generalized Wendland (GW) class, under fixed domain asymptotics. As for the Matern case, this class allows for a continuous parameterization of smoothness of the underlying Gaussian random field, being additionally compactly supported. The paper is divided into three parts: first, we characterize the equivalence of two Gaussian measures with GW covariance function, and we provide sufficient con...
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作者：Patschkowski, Tim; Rohde, Angelika
作者单位：Ruhr University Bochum; University of Freiburg
摘要：We develop honest and locally adaptive confidence bands for probability densities. They provide substantially improved confidence statements in case of inhomogeneous smoothness, and are easily implemented and visualized. The article contributes conceptual work on locally adaptive inference as a straightforward modification of the global setting imposes severe obstacles for statistical purposes. Among others, we introduce a statistical notion of local Holder regularity and prove a corresponding...
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作者：Fan, Zhou; Guan, Leying
作者单位：Stanford University
摘要：We study recovery of piecewise-constant signals on graphs by the estimator minimizing an l(0)-edge-penalized objective. Although exact minimization of this objective may be computationally intractable, we show that the same statistical risk guarantees are achieved by the alpha-expansion algorithm which computes an approximate minimizer in polynomial time. We establish that for graphs with small average vertex degree, these guarantees are minimax rate-optimal over classes of edge-sparse signals...
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作者：Fan, Jianqing; Liu, Han; Wang, Weichen
作者单位：Princeton University; Fudan University
摘要：We propose a general Principal Orthogonal complEment Thresholding (POET) framework for large-scale covariance matrix estimation based on the approximate factor model. A set of high-level sufficient conditions for the procedure to achieve optimal rates of convergence under different matrix norms is established to better understand how POET works. Such a framework allows us to recover existing results for sub-Gaussian data in a more transparent way that only depends on the concentration properti...
