-
作者：Einmahl, John H. J.; Segers, Johan
作者单位：Tilburg University; Universite Catholique Louvain
摘要：Consider a random sample from a bivariate distribution function F in the max-domain of attraction of all extreme-value distribution function G. This G is characterized by two extreme-value indices and a spectral measure, the latter determining the tail dependence structure of F. A major issue in multivariate extreme-value theory is the estimation of the spectral measure (1)p with respect to the L-p norm. For every p is an element of [1, infinity], a nonparametric maximum empirical likelihood e...
-
作者：Genest, Christian; Segers, Johan
作者单位：Laval University; Universite Catholique Louvain; Tilburg University
摘要：Consider a continuous random pair (X, Y) whose dependence is characterized by an extreme-value copula with Pickands dependence function A. When the marginal distributions of X and Y are known, several consistent estimators of A are available. Most of them are variants of the estimators due to Pickands [Bull. Inst. Internat. Statist. 49 (1981) 859-878.] and Caperaa, Fougeres and Genest [Biometrika 84 (1997) 567-577]. In this paper, rank-based versions of these estimators are proposed for the mo...
-
作者：Ali, R. Ayesha; Richardson, Thomas S.; Spirtes, Peter
作者单位：University of Guelph; University of Washington; University of Washington Seattle; Carnegie Mellon University
摘要：Ancestral graphs can encode conditional independence relations that arise in directed acyclic graph (DAG) models with latent and selection variables. However, for any ancestral graph, there may be several other graphs to which it is Markov equivalent. We state and prove conditions under which two maximal ancestral graphs are Markov equivalent to each other, thereby extending analogous results for DAGs given by other authors. These conditions lead to an algorithm for determining Markov equivale...
-
作者：Liang, Faming
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang, Liu and Carroll [J. Amer Statist. Assoc. 102 (2007) 305-320] as a general simulation and optimization algorithm. In this paper, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. The new algorithm is tested through a change-point identification example. The numerical results indicate that the new algorithm can outperf...
-
作者：Rinaldo, Alessandro
作者单位：Carnegie Mellon University
摘要：We consider estimating an unknown signal, both blocky and sparse, which is corrupted by additive noise. We study three interrelated least squares procedures and their asymptotic properties. The first procedure is the fused lasso, put forward by Friedman et al. [Ann. Appl. Statist. 1 (2007) 302-332], which we modify into a different estimator, called the fused adaptive lasso, with better properties. The other two estimators we discuss solve least squares problems on sieves; one constrains the m...
-
作者：Davies, P. L.; Kovac, A.; Meise, M.
作者单位：University of Duisburg Essen; University of Bristol; Eindhoven University of Technology
摘要：In this paper we offer a unified approach to the problem of nonparametric regression on the unit interval. It is based on a universal, honest and nonasymptotic confidence region A(n) which is defined by a set of linear in-equalities involving the values of the functions at the design points. Interest will typically center on certain simplest functions in A(n) where simplicity can be defined in terms of shape (number of local extremes, intervals of convexity/concavity) or smoothness (bounds on ...
-
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...
-
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...
-
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,
-
作者：van der Vaart, A. W.; van Zanten, J. H.
作者单位：Vrije Universiteit Amsterdam
摘要：We consider nonparametric Bayesian estimation inference using a rescaled smooth Gaussian field as a prior for a multidimensional function. The rescaling is achieved using a Gamma variable and the procedure can be viewed as choosing all inverse Gamma bandwidth. The procedure is studied from a frequentist perspective in three statistical settings involving replicated observations (density estimation, regression and classification). We prove that the resulting posterior distribution shrinks to th...
