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作者：Jing, Bing-Yi; Pan, Guangming; Shao, Qi-Man; Zhou, Wang
作者单位：Hong Kong University of Science & Technology; Nanyang Technological University; National University of Singapore
摘要：The density function of the limiting spectral distribution of general sample covariance matrices is usually unknown. We propose to use kernel estimators which are proved to be consistent. A simulation study is also conducted to show the performance of the estimators.
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作者：Ait-Sahalia, Yacine; Jacod, Jean
作者单位：Princeton University; National Bureau of Economic Research; Sorbonne Universite; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
摘要：This paper considers the problem of testing for the presence of a continuous part in a semimartingale sampled at high frequency. We provide two tests, one where the null hypothesis is that a continuous component is present, the other where the continuous component is absent, and the model is then driven by a pure jump process. When applied to high-frequency individual stock data, both tests point toward the need to include a continuous component in the model.
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作者：Reynaud-Bouret, Patricia; Schbath, Sophie
作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Cote d'Azur; Universite Paris Saclay; INRAE
摘要：The aim of this paper is to provide a new method for the detection of either favored or avoided distances between genomic events along DNA sequences. These events are modeled by a Hawkes process. The biological problem is actually complex enough to need a nonasymptotic penalized model selection approach. We provide a theoretical penalty that satisfies an oracle inequality even for quite complex families of models. The consecutive theoretical estimator is shown to be adaptive minimax for Holder...
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作者：Brown, Lawrence D.; Cai, T. Tony; Zhou, Harrison H.
作者单位：University of Pennsylvania; Yale University
摘要：Most results in nonparametric regression theory are developed only for the case of additive noise. In such a setting many smoothing techniques including wavelet thresholding methods have been developed and shown to be highly adaptive. In this paper we consider nonparametric regression in exponential families with the main focus on the natural exponential families with a quadratic variance function, which include, for example, Poisson regression, binomial regression and gamma regression. We pro...
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作者：Huckemann, Stephan F.; Kim, Peter T.; Koo, Ja-Yong; Munk, Axel
作者单位：University of Gottingen; University of Guelph; Korea University
摘要：In this paper we consider a novel statistical inverse problem on the Poincare, or Lobachevsky, upper (complex) half plane. Here the Riemannian structure is hyperbolic and a transitive group action comes from the space of 2 x 2 real matrices of determinant one via Mobius transformations. Our approach is based on a deconvolution technique which relies on the Helgason-Fourier calculus adapted to this hyperbolic space. This gives a minimax nonparametric density estimator of a hyperbolic density th...
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作者：Moreno, Elias; Javier Giron, F.; Casella, George
作者单位：University of Granada; Universidad de Malaga; State University System of Florida; University of Florida
摘要：In the class of normal regression models with a finite number of regressors, and for a wide class of prior distributions, a Bayesian model selection procedure based on the Bayes factor is consistent [Casella and Moreno J. Amer Statist. Assoc. 104 (2009) 1261-1271]. However, in models where the number of parameters increases as the sample size increases, properties of the Bayes factor are not totally understood. Here we study consistency of the Bayes factors for nested normal linear models when...
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作者：Verzelen, Nicolas
作者单位：INRAE; Institut Agro; Montpellier SupAgro; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
摘要：We study the nonparametric covariance estimation of a stationary Gaussian field X observed on a regular lattice. In the time series setting, some procedures like AIC are proved to achieve optimal model selection among autoregressive models. However, there exists no such equivalent results of adaptivity in a spatial setting. By considering collections of Gaussian Markov random fields (GMRF) as approximation sets for the distribution of X. we introduce a novel model selection procedure for spati...
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作者：Gine, Evarist; Nickl, Richard
作者单位：University of Connecticut; University of Cambridge
摘要：Given a sample from some unknown continuous density f : R -> R, we construct adaptive confidence bands that are honest for all densities in a generic subset of the union of t-Holder balls, 0 < t <= r, where r is a fixed but arbitrary integer. The exceptional (nongeneric) set of densities for which our results do not hold is shown to be nowhere dense in the relevant Holder-norm topologies. In the course of the proofs we also obtain limit theorems for maxima of linear wavelet and kernel density ...
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作者：Wei, Ying
作者单位：Columbia University
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作者：Kim, Min Hee; Akritas, Michael G.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：A new thresholding method, based on L-statistics and called order thresholding, is proposed as a technique for improving the power when testing against high-dimensional alternatives. The new method allows great flexibility in the choice of the threshold parameter. This results in improved power over the soft and hard thresholding methods. Moreover, order thresholding is not restricted to the normal distribution. An extension of the basic order threshold statistic to high-dimensional ANOVA is p...
