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作者：Panaretos, Victor M.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：We formulate and investigate a statistical inverse problem of a random tomographic nature, where a probability density function on R-3 is to be recovered from observation of finitely many of its two-dimensional projections in random and unobservable directions. Such a problem is distinct from the classic problem of tomography where both the projections and the unit vectors normal to the projection plane are observable. The problem arises in single particle electron microscopy, a powerful metho...
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作者：Zhao, Peng; Rocha, Guilherme; Yu, Bin
作者单位：University of California System; University of California Berkeley
摘要：Extracting useful information from high-dimensional data is an important focus of today's statistical research and practice. Penalized loss function minimization has been shown to be effective for this task both theoretically and empirically. With the virtues of both regularization and sparsity, the L-1-penalized squared error minimization method Lasso has been popular in regression models and beyond. In this paper, we combine different norms including L-1 to form an intelligent penalty in ord...
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作者：Amini, Arash A.; Wainwright, Martin J.
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：Principal component analysis (PCA) is a classical method for dimensionality reduction based oil extracting the dominant eigenvectors of the Sample covariance matrix. However, PCA is well known to behave poorly inw the large p, small n setting, in which the problem dimension p is comparable to or larger than the sample size n. This paper studies PICA ill this high-dimensional regime, but under the additional assumption that the maximal eigenvector is sparse, say, with at most k nonzero componen...
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作者：Bardet, Jean-Marc; Wintenberger, Olivier
作者单位：heSam Universite; Universite Pantheon-Sorbonne
摘要：Strong consistency and asymptotic normality of the quasi-maximum likelihood estimator are given for a general class of multidimensional causal processes. For particular cases already studied in the literature [for instance univariate or multivariate ARCH(infinity) processes], the assumptions required for establishing these results are often weaker than existing conditions. The QMLE asymptotic behavior is also given for numerous new examples of univariate or multivariate processes (for instance...
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作者：Lin, Zhengyan; Liu, Weidong
作者单位：Zhejiang University
摘要：We consider the limit distribution of maxima of periodograms for stationary processes. Our method is based on m-dependent approximation for stationary processes and a moderate deviation result,
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作者：Dette, Holger; Titoff, Stefanie
作者单位：Ruhr University Bochum
摘要：We consider the problem of constructing optimal designs for model discrimination between competing regression models. Various new properties of optimal designs with respect to the popular T-optimality criterion are derived, which in many circumstances allow an explicit determination of T-optimal designs. It is also demonstrated, that in nested linear models the number of support points of T-optimal designs is usually too small to estimate all parameters in the extended model. In many cases T-o...
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作者：Cohen, Arthur; Sackrowitz, Harold B.; Xu, Minya
作者单位：Rutgers University System; Rutgers University New Brunswick; Peking University
摘要：The most popular multiple testing procedures are stepwise procedures based on P-values for individual test statistics. Included among these are the false discovery rate (FDR) controlling procedures of Benjamini-Hochberg [J. Roy. Statist. Soc. Ser B 57 (1995) 289-300] and their offsprings. Even for models that entail dependent data, P-values based on marginal distributions are used. Unlike such methods, the new method takes dependency into account at all stages. Furthermore, the P-value procedu...
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作者：Andrieu, Christophe; Roberts, Gareth O.
作者单位：University of Bristol; University of Warwick
摘要：We introduce a powerful and flexible MCMC algorithm for stochastic simulation. The method builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139-1160], showing how algorithms which are approximations to an idealized marginal algorithm, can share the same marginal stationary distribution as the idealized method. Theoretical results are given describing the convergence properties of the proposed method, and simple numerical examples are given to illustrate the prom...
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作者：Baraud, Yannick; Giraud, Christophe; Huet, Sylvie
作者单位：Universite Cote d'Azur; INRAE
摘要：Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean p of Y by model selection. More precisely, we start with a collection S = {S(m), m is an element of M} of linear subspaces of R(n) and associate to each of these the least-squares estimator of mu on S(m). Then, we use a data driven penalized criterion in order to select one estimator among these. Our first objective is to analyze the performance of estimato...
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作者：Yang, Min; Stufken, John
作者单位：University of Missouri System; University of Missouri Columbia; University System of Georgia; University of Georgia
摘要：We propose a new approach for identifying the support points of a locally optimal design when the model is a nonlinear model. In contrast to the commonly used geometric approach, we use an approach based on algebraic tools. Considerations are restricted to models with two parameters, and the general results are applied to often used special cases, including logistic, probit, double exponential and double reciprocal models for binary data, a loglinear Poisson regression model for count data, an...
