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Showing papers in "Journal of Multivariate Analysis in 2010"


Journal ArticleDOI
TL;DR: This article studies the maximum likelihood inference on a class of Wiener processes with random effects for degradation data, one on which n independent subjects, each with a Wiener process with random drift and diffusion parameters, are observed at different times.

346 citations


Journal ArticleDOI
TL;DR: The extremal dependence of a copula, as described by its extreme value copulas, is shown to be completely determined by its tail dependence functions.

287 citations


Journal ArticleDOI
TL;DR: This work expresses conditions under which a specific pair-copula decomposition of a multivariate distribution is of this simplified form and shows that the simplified PCC in fact is a rather good approximation, even when the simplifying assumption is far from being fulfilled by the actual model.

242 citations


Journal ArticleDOI
TL;DR: This work proposes modeling the conditional quantile by a single-index function g"0(x^[email protected]"0), where a univariate link function g’0(@?) is applied to a linear combination of covariates x^ [email protected]"0, often called the single- index.

168 citations


Journal ArticleDOI
TL;DR: Two tests are proposed that perform well and better in several cases than the other two tests available in the literature and can be used in all situations, including when the likelihood ratio test is available.

140 citations


Journal ArticleDOI
TL;DR: In this article, the authors prove that the empirical spectral distribution of a complex random nxn matrix converges to the uniform distribution over the unit disk in the complex plane under the finite fourth moment assumption on matrix elements.

120 citations


Journal ArticleDOI
TL;DR: The universal consistency of the bagged (bootstrap-aggregated) nearest neighbour method for regression and classification is shown, defined as an average over the Y"i's corresponding to those X"i which are LNN of x.

118 citations


Journal ArticleDOI
TL;DR: This work proposes to generalize the method of kriging when data are spatially sampled curves by constructing a spatial functional linear model including spatial dependencies between curves.

116 citations


Journal ArticleDOI
TL;DR: Weak convergence of the empirical residual process to a Gaussian process is proved and the estimation of the error distribution in a heteroscedastic nonparametric regression model with multivariate covariates is considered.

97 citations


Journal ArticleDOI
TL;DR: This paper introduces bivariate Birnbaum-Saunders distribution which is an absolutely continuous distribution whose marginals are univariate Birnsaunders distributions, and presents the asymptotic distributions of the maximum likelihood estimators and uses them to construct confidence intervals for the parameters.

87 citations


Journal ArticleDOI
TL;DR: This work proposes a method for testing the constancy of @J against a change-point alternative which uses the functional principal component analysis and develops a new truncation approach which together with Mensov's inequality can be used in other problems of functional time series analysis.

Journal ArticleDOI
TL;DR: A new formula for Kendall's tau of Liouville copulas is derived and a new dependence ordering for non-negative random variables is introduced which generalises the Laplace transform order.

Journal ArticleDOI
TL;DR: The proposed adjustment has a considerable advantage in small sample inference, especially in estimating the shrinkage parameters and in constructing the parametric bootstrap prediction intervals of the small area means, which require the use of a strictly positive consistent model variance estimate.

Journal ArticleDOI
TL;DR: This paper focuses on the variable selections for semiparametric varying coefficient partially linear models when the covariates in the parametric and nonparametric components are all measured with errors, and a bias-corrected variable selection procedure is proposed by combining basis function approximations with shrinkage estimations.

Journal ArticleDOI
TL;DR: An advantage of this model is that its parameters directly affect the level of dependence between each pair of components of the random vector, and as such the parameters of the model are more interpretable than those of earlier parametric models for multivariate extremes.

Journal ArticleDOI
TL;DR: It is shown that naive extensions of comonotonicity do not enjoy some of the main properties of the univariate concept, and in order to have these properties, more structures are needed than inThe univariate case.

Journal ArticleDOI
TL;DR: The aim of this paper is to study asymptotic properties of the kernel regression estimate whenever functional stationary ergodic data are considered, and establishes the consistency in probability, with a rate, as well as the asymPTotic normality which induces a confidence interval for the regression function usable in practice since it does not depend on any unknown quantity.

Journal ArticleDOI
TL;DR: A universal conditional distribution method for uniform sampling from n-spheres and n-balls is described, based on properties of a family of radially symmetric multivariate distributions, which provides a unifying view on several known algorithms as well as enabling us to construct novel variants.

Journal ArticleDOI
TL;DR: A probabilistic interpretation for hierarchical Archimedean copulas based on Levy subordinators is given and suggests an efficient sampling algorithm and allows one to easily construct several new parametric families of hierarchical ArchIMedeanCopula copulas.

Journal ArticleDOI
TL;DR: A three-dimensional landmark-based data set is used from a longitudinal orthodontic study, and the persistent homology method is able to distinguish clinically relevant treatment effects, and comparisons are made with the traditional landmark- based statistical shape analysis methods of Dryden and Mardia, and Euclidean Distance Matrix Analysis.

Journal ArticleDOI
TL;DR: The simulation results show that the new test procedure for sphericity of the covariance matrix when the dimensionality, p, exceeds that of the sample size, N=n+1.

Journal ArticleDOI
TL;DR: In this paper, the authors used a coupling technique introduced in 1983 by Bradley [R.C. Bradley, Approximation theorems for strongly mixing random variables, Michigan Math. 30 (1983),69-81] to prove a new generalized covariance inequality similar to Yoshihara's [K. Verw. Gebiete 35 (1976), 237-252].

Journal ArticleDOI
TL;DR: This work addresses the estimation of quantiles from heavy-tailed distributions when functional covariate information is available and in the case where the order of the quantile converges to one as the sample size increases.

Journal ArticleDOI
TL;DR: An application of the findings in this paper to a mixture model to classify a dataset into two clusters and provides consistency properties of the eigenvalue estimation as well as its limiting distribution when the dimension d and the sample size n both grow to infinity in such a way that n is much lower than d.

Journal ArticleDOI
Jan R. Magnus1
TL;DR: This paper discusses how to generalize the concept of vector derivative to matrix derivative, proposes two definitions, a 'broad' and a 'narrow' one, and compares the two definitions and argues in favor of the narrow definition.

Journal ArticleDOI
TL;DR: The L"1-consistency of Dirichlet mixutures in the multivariate density estimation setting is extended and the Kullback-Leibler property of the prior holds and the size of the sieve in the parameter space in terms of L" 1-metric entropy is not larger than the order of n.

Journal ArticleDOI
TL;DR: In this paper, a smoothed jackknife empirical likelihood method to construct confidence intervals for the receiver operating characteristic (ROC) curve is proposed and Wilks' theorem for the empirical likelihood ratio statistic is proved.

Journal ArticleDOI
TL;DR: This work considers the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding, and proves that these estimators are minimax.

Journal ArticleDOI
TL;DR: The Tukey depth is extended to distributions with smooth depth contours, with elliptically symmetric distributions as special cases, and the key ingredient of the proofs is the well-known Cramer-Wold theorem.

Journal ArticleDOI
TL;DR: A simple subset of the Markov basis is shown which connects all fibers with a positive sample size for each combination of levels of covariates in the case of bivariate logistic regression.