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作者：Efromovich, Sam
作者单位：University of Texas System; University of Texas Dallas
摘要：The theory of adaptive estimation and oracle inequalities for the case of Gaussian-shift-finite-interval experiments has made significant progress in recent years. In particular, sharp-minimax adaptive estimators and exact exponential-type oracle inequalities have been suggested for a vast set of functions including analytic and Sobolev with any positive index as well as for Efromovich-Pinsker and Stein blockwise-shrinkage estimators. Is it possible to obtain similar results for a more interes...
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作者：Jupp, P. E.
作者单位：University of St Andrews
摘要：Data-driven versions of Sobolev tests of uniformity on compact Riemannian manifolds are proposed. These tests are invariant under isometries; and are consistent against all alternatives. The large-sample asymptotic null distributions are given.
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作者：Bickel, Peter J.; Levina, Elizaveta
作者单位：University of California System; University of California Berkeley; University of Michigan System; University of Michigan
摘要：This paper considers regularizing a covariance matrix of p variables estimated from it observations, by hard thresholding. We show that the thresholded estimate is consistent in the operator norm as long as the true covariance matrix is sparse in a suitable sense, the variables are Gaussian or sub-Gaussian, and (log p)/n -> 0, and obtain explicit rates. The results are uniform over families of covariance matrices which satisfy a fairly natural notion of sparsity. We discuss an intuitive resamp...
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作者：Buehlmann, Peter; Meier, Lukas
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
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作者：Bickel, Peter
作者单位：University of California System; University of California Berkeley
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作者：Rao, N. Raj; Mingo, James A.; Speicher, Roland; Edelman, Alan
作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Queens University - Canada; Massachusetts Institute of Technology (MIT)
摘要：We consider settings where the observations are drawn from a zero-mean multivariate (real or complex) normal distribution with the population covariance matrix having eigenvalues of arbitrary multiplicity. We assume that the eigenvectors of the population covariance matrix are unknown and focus on inferential procedures that are based on the sample eigenvalues alone (i.e., eigen-inference). Results found in the literature establish the asymptotic normality of the fluctuation in the trace of po...
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作者：Radchenko, Peter
作者单位：University of Southern California
摘要：A general method is presented for deriving the limiting behavior of estimators that are defined as the values of parameters optimizing an empirical criterion function. The asymptotic behavior of such estimators is typically deduced from uniform limit theorems for rescaled and reparametrized criterion functions. The new method can handle cases where the standard approach does not yield the complete limiting behavior of the estimator. The asymptotic analysis depends on a decomposition of criteri...
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作者：Wu, Wei Biao
作者单位：University of Chicago
摘要：A popular framework for false discovery control is the random effects model in which the null hypotheses are assumed to be independent. This paper generalizes the random effects model to a conditional dependence model which allows dependence between null hypotheses. The dependence can be useful to characterize the spatial structure of the null hypotheses. Asymptotic properties of false discovery proportions and numbers of rejected hypotheses are explored and a large-sample distributional theor...
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作者：Douc, Randal; Moulines, Eric
作者单位：Institut Polytechnique de Paris; Ecole Polytechnique
摘要：In the last decade, sequential Monte Carlo methods (SMC) emerged as a key tool in computational statistics [see, e.g., Sequential Monte Carlo Methods in Practice (2001) Springer, New York, Monte Carlo Stratergies in Scientific Computing (2001) Springer, New York, Complex Stochastic systems (2001) 109-173]. These algorithms approximate a sequence of distributions by a sequence of weighted empirical measures associated to a weighted population of particles, which are generated recursively. Despi...
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作者：Zhang, Chunming; Yu, Tao
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：Functional magnetic resonance imaging (fMRI) aims to locate activated regions in human brains when specific tasks are performed. The conventional tool for analyzing fMRI data applies some variant of the linear model, which is restrictive in modeling assumptions. To yield more accurate prediction of the time-course behavior of neuronal responses, the semiparametric inference for the underlying hemodynamic response function is developed to identify significantly activated voxels. Under mild regu...
