当前期刊: Journal of Multivariate Analysis Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
我的关注
我的收藏
您暂时未登录!
登录
  • Non-asymptotic error controlled sparse high dimensional precision matrix estimation
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-10-03
    Adam B. Kashlak

    Estimation of a high dimensional precision matrix is a critical problem to many areas of statistics including Gaussian graphical models and inference on high dimensional data. Working under the structural assumption of sparsity, we propose a novel methodology for estimating such matrices while controlling the false positive rate, percentage of matrix entries incorrectly chosen to be non-zero. We specifically

    更新日期:2020-10-17
  • On the estimation of entropy in the FastICA algorithm
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-10-09
    Elena Issoglio; Paul Smith; Jochen Voss

    The fastICA method is a popular dimension reduction technique used to reveal patterns in data. Here we show both theoretically and in practice that the approximations used in fastICA can result in patterns not being successfully recognised. We demonstrate this problem using a two-dimensional example where a clear structure is immediately visible to the naked eye, but where the projection chosen by

    更新日期:2020-10-17
  • Multiply robust subgroup identification for longitudinal data with dropouts via median regression
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-10-07
    Wenqi Lu; Guoyou Qin; Zhongyi Zhu; Dongsheng Tu

    Subgroup identification serves as an important step towards precision medicine which has attracted great attention recently. On the other hand, longitudinal data with dropouts often arises in medical research. However there is little work in subgroup identification considering this data type. Therefore, in this paper we propose a new subgroup identification method based on concave fusion penalization

    更新日期:2020-10-16
  • Splitting models for multivariate count data
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-28
    Jean Peyhardi; Pierre Fernique; Jean-Baptiste Durand

    We investigate the class of splitting distributions as the composition of a singular multivariate distribution and a univariate distribution. It will be shown that most common parametric count distributions (multinomial, negative multinomial, multivariate hypergeometric, multivariate negative hypergeometric, …) can be written as splitting distributions with separate parameters for both components,

    更新日期:2020-10-11
  • Family of mean-mixtures of multivariate normal distributions: Properties, inference and assessment of multivariate skewness
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-25
    Me’raj Abdi; Mohsen Madadi; Narayanaswamy Balakrishnan; Ahad Jamalizadeh

    In this paper, a new mixture family of multivariate normal distributions, formed by mixing multivariate normal distribution and a skewed distribution, is constructed. Some properties of this family, such as characteristic function, moment generating function, and the first four moments are derived. The distributions of affine transformations and canonical forms of the model are also derived. An EM-type

    更新日期:2020-10-07
  • Robustness and asymptotics of the projection median
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-18
    Kelly Ramsay; Stephane Durocher; Alexandre Leblanc

    The projection median as introduced by Durocher and Kirkpatrick (2005); Durocher and Kirkpatrick (2009) is a robust multivariate, nonparametric location estimator. It is a weighted average of points in a sample, where each point’s weight is proportional to the fraction of directions in which that point is a univariate median. The projection median has the highest possible asymptotic breakdown and is

    更新日期:2020-10-02
  • Scale matrix estimation of an elliptically symmetric distribution in high and low dimensions
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-23
    Anis M. Haddouche; Dominique Fourdrinier; Fatiha Mezoued

    The problem of estimating the scale matrix Σ in a multivariate additive model, with elliptical noise, is considered from a decision-theoretic point of view. As the natural estimators of the form Σˆa=aS (where S is the sample covariance matrix and a is a positive constant) perform poorly, we propose estimators of the general form Σˆa,G=a(S+SS+G(Z,S)), where S+ is the Moore–Penrose inverse of S and G(Z

    更新日期:2020-10-02
  • Estimating an extreme Bayesian network via scalings
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-17
    Claudia Klüppelberg; Mario Krali

    A recursive max-linear vector models causal dependence between its components by expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph and some exogenous innovation. Motivated by extreme value theory, innovations are assumed to have regularly varying distribution tails. We propose a scaling technique in order to determine a causal order of the node

    更新日期:2020-10-02
  • Nonlinear and additive principal component analysis for functional data
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-09
    Jun Song; Bing Li

    We introduce a nonlinear additive functional principal component analysis (NAFPCA) for vector-valued functional data. This is a generalization of functional principal component analysis and allows the relations among the random functions involved to be nonlinear. The method is constructed via two additively nested Hilbert spaces of functions, in which the first space characterizes the functional nature

    更新日期:2020-09-28
  • Kernel density estimation on symmetric spaces of non-compact type
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-09
    Dena Marie Asta

    We construct a kernel density estimator on symmetric spaces of non-compact type and establish an upper bound for its convergence rate, analogous to the minimax rate for classical kernel density estimators on Euclidean space. Symmetric spaces of non-compact type include hyperboloids of constant curvature −1 and spaces of symmetric positive definite matrices. This paper obtains a simplified formula in

    更新日期:2020-09-10
  • Testing high dimensional covariance matrices via posterior Bayes factor
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-09
    Zhendong Wang, Xingzhong Xu

    With the advent of the era of big data, high dimensional covariance matrices are increasingly encountered and testing covariance structure has become an active area in contemporary statistical inference. Conventional testing methods fail when addressing high dimensional data due to the singularity of the sample covariance matrices. In this paper, we propose a novel test for the prominent identity test

    更新日期:2020-09-09
  • The consistency and asymptotic normality of the kernel type expectile regression estimator for functional data
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-09-09
    Mustapha Mohammedi, Salim Bouzebda, Ali Laksaci

    The aim of this paper is to nonparametrically estimate the expectile regression in the case of a functional predictor and a scalar response. More precisely, we construct a kernel-type estimator of the expectile regression function. The main contribution of this study is the establishment of the asymptotic properties of the expectile regression estimator. Precisely, we establish the almost complete

    更新日期:2020-09-09
  • A note on the regularity of optimal-transport-based center-outward distribution and quantile functions
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-28
    Eustasio del Barrio, Alberto González-Sanz, Marc Hallin

    We provide sufficient conditions under which the center-outward distribution and quantile functions introduced in Chernozhukov et al. (2017) and Hallin (2017) are homeomorphisms, thereby extending a recent result by Figalli (2018). Our approach relies on Caffarelli’s classical regularity theory for the solutions of the Monge–Ampère equation, but has to deal with difficulties related with the unboundedness

    更新日期:2020-08-28
  • On the structure of exchangeable extreme-value copulas
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-21
    Jan-Frederik Mai, Matthias Scherer

    We show that the set of d-variate symmetric stable tail dependence functions is a simplex and we determine its extremal boundary. The subset of elements which arises as d-margins of the set of (d+k)-variate symmetric stable tail dependence functions is shown to be proper for arbitrary k≥1. Finally, we derive an intuitive and useful necessary condition for a bivariate extreme-value copula to arise as

    更新日期:2020-08-21
  • Dimensionality reduction for binary data through the projection of natural parameters
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-19
    Andrew J. Landgraf, Yoonkyung Lee

    Principal component analysis (PCA) for binary data, known as logistic PCA, has become a popular alternative to dimensionality reduction of binary data. It is motivated as an extension of ordinary PCA by means of a matrix factorization, akin to the singular value decomposition, that maximizes the Bernoulli log-likelihood. We propose a new formulation of logistic PCA which extends Pearson’s formulation

    更新日期:2020-08-19
  • Single-index composite quantile regression for massive data
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-19
    Rong Jiang, Keming Yu

    Composite quantile regression (CQR) is becoming increasingly popular due to its robustness from quantile regression. Recently, the CQR method has been studied extensively with single-index models. However, the numerical inference of CQR methods for single-index models must involve iteration. In this study, we propose a non-iterative CQR (NICQR) estimation algorithm and derive the asymptotic distribution

    更新日期:2020-08-19
  • Testing for spherical and elliptical symmetry
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-17
    Isaia Albisetti, Fadoua Balabdaoui, Hajo Holzmann

    We construct new testing procedures for spherical and elliptical symmetry based on the characterization that a random vector X with finite mean has a spherical distribution if and only if E[u⊤X|v⊤X]=0 holds for any two perpendicular vectors u and v. Our test is based on the Kolmogorov–Smirnov statistic, and its rejection region is found via the spherically symmetric bootstrap. We show the consistency

    更新日期:2020-08-17
  • Bivariate gamma model
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-06
    Ruijian Han, Kani Chen, Chunxi Tan

    Among undirected graph models, the β-model plays a fundamental role and is widely applied to analyze network data. It assumes the edge probability is linked with the sum of the strength parameters of the two vertices through a sigmoid function. Because of the univariate nature of the link function, this formulation, despite its popularity, can be too restrictive for practical applications, even with

    更新日期:2020-08-06
  • Locally optimal designs for multivariate generalized linear models
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-06
    Osama Idais

    The multivariate generalized linear model is considered. Each univariate response follows a generalized linear model. In this situation, the linear predictors and the link functions are not necessarily the same. The quasi-Fisher information matrix is obtained by using the method of generalized estimating equations. Then locally optimal designs for multivariate generalized linear models are investigated

    更新日期:2020-08-06
  • Asymptotics and practical aspects of testing normality with kernel methods
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-08-04
    Natsumi Makigusa, Kanta Naito

    This paper is concerned with testing normality in a Hilbert space based on the maximum mean discrepancy. Specifically, we discuss the behavior of the test from two standpoints: asymptotics and practical aspects. Asymptotic normality of the test under a fixed alternative hypothesis is developed, which implies that the test has consistency. Asymptotic distribution of the test under a sequence of local

    更新日期:2020-08-04
  • Surface functional models
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-07-30
    Ziqi Chen, Jianhua Hu, Hongtu Zhu

    The aim of this paper is to develop a new framework of surface functional models (SFM) for surface functional data which contains repeated observations in two domains (typically, time-location). The primary problem of interest is to investigate the relationship between a response and the two domains, where the numbers of observations in both domains within a subject may be diverging. The SFMs are far

    更新日期:2020-07-30
  • Nonlinear functional canonical correlation analysis via distance covariance
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-07-29
    Hanbing Zhu, Rui Li, Riquan Zhang, Heng Lian

    Functional canonical correlation analysis (FCCA) is a tool for exploring the associations between a pair of functional data. However, when the association is nonlinear or even nonmonotone, FCCA can fail to discover any meaningful relationship between the pair. In this paper, nonlinear FCCA estimators are constructed based on some popular measures of dependence — distance covariance and distance correlation

    更新日期:2020-07-29
  • Copula-based regression models with data missing at random
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-07-22
    Shigeyuki Hamori, Kaiji Motegi, Zheng Zhang

    The existing literature of copula-based regression assumes that complete data are available, but this assumption is violated in many real applications. The present paper allows the regressand and regressors to be missing at random (MAR). We formulate a generalized regression model which unifies many prominent cases such as the conditional mean and quantile regressions. A semiparametric copula and the

    更新日期:2020-07-22
  • Estimating sparse networks with hubs
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-07-09
    Annaliza McGillivray, Abbas Khalili, David A. Stephens

    Graphical modelling techniques based on sparse estimation have been applied to infer complex networks in many fields, including biology and medicine, engineering, finance and social sciences. One structural feature of some of these networks that poses a challenge for statistical inference is the presence of a small number of strongly interconnected nodes, which are called hubs. For example, in microbiome

    更新日期:2020-07-09
  • Bayesian shrinkage estimation of negative multinomial parameter vectors
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-07-04
    Yasuyuki Hamura, Tatsuya Kubokawa

    The negative multinomial distribution is a multivariate generalization of the negative binomial distribution. In this paper, we consider the problem of estimating an unknown matrix of probabilities on the basis of observations of negative multinomial variables under the standardized squared error loss. First, a general sufficient condition for a shrinkage estimator to dominate the UMVU estimator is

    更新日期:2020-07-04
  • Multivariate tests of independence and their application in correlation analysis between financial markets
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-30
    Long Feng, Xiaoxu Zhang, Binghui Liu

    We consider the multivariate independence testing problem between pairs of random vectors for high-dimensional data and develop three high-dimensional nonparametric independence tests based on spatial sign and spatial rank, which have greater power than many existing popular tests, especially for heavy-tailed distributions. Under the elliptically symmetric distributions, which are much more general

    更新日期:2020-06-30
  • Scale and shape mixtures of matrix variate extended skew normal distributions
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-23
    Amir Rezaei, Fatemeh Yousefzadeh, Reinaldo B. Arellano-Valle

    In this paper, we propose a matrix extension of the scale and shape mixtures of multivariate skew normal distributions and present some particular cases of this new class. We also present several formal properties of this class, such as the marginal distributions, the moment generating function, the distribution of linear and quadratic forms, and the selection and stochastic representations. In addition

    更新日期:2020-06-23
  • A Conway–Maxwell-multinomial distribution for flexible modeling of clustered categorical data
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-15
    Darcy Steeg Morris, Andrew M. Raim, Kimberly F. Sellers

    Categorical data are often observed as counts resulting from a fixed number of trials in which each trial consists of making one selection from a prespecified set of categories. The multinomial distribution serves as a standard model for such data but assumes that trials are independent and identically distributed. Extensions such as the Dirichlet-multinomial and random-clumped multinomial distribution

    更新日期:2020-06-15
  • Testing for the significance of functional covariates
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-10
    Samuel Maistre, Valentin Patilea

    We consider the problem of testing for the nullity of conditional expectations of Hilbert space-valued random variables. We allow for conditioning variables taking values in finite or infinite Hilbert spaces. This testing problem occurs, for instance, when checking the goodness-of-fit or the effect of some infinite-dimensional covariates in regression models for functional data. Testing the independence

    更新日期:2020-06-10
  • Spearman rank correlation of the bivariate Student t and scale mixtures of normal distributions
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-10
    Andréas Heinen, Alfonso Valdesogo

    We derive an expression for the Spearman rank correlation of bivariate scale mixtures of normals (SMN) and we show that within this class, for any value of the correlation parameter, the Spearman rank correlation of the normal is the greatest in absolute value. We then provide expressions for the symmetric generalized hyperbolic, the Bessel, and the Laplace distributions. We further derive an expression

    更新日期:2020-06-10
  • Continuous time hidden Markov model for longitudinal data
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-10
    Jie Zhou, Xinyuan Song, Liuquan Sun

    Hidden Markov models (HMMs) describe the relationship between two stochastic processes, namely, an observed outcome process and an unobservable finite-state transition process. Given their ability to model dynamic heterogeneity, HMMs are extensively used to analyze heterogeneous longitudinal data. A majority of early developments in HMMs assume that observation times are discrete and regular. This

    更新日期:2020-06-10
  • Univariate likelihood projections and characterizations of the multivariate normal distribution
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-10
    Albert Vexler

    The problem of characterizing a multivariate distribution of a random vector using examination of univariate combinations of vector components is an essential issue of multivariate analysis. The likelihood principle plays a prominent role in developing powerful statistical inference tools. In this context, we raise the question: can the univariate likelihood function based on a random vector be used

    更新日期:2020-06-10
  • Computation of the expected Euler characteristic for the largest eigenvalue of a real non-central Wishart matrix
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-09
    Nobuki Takayama, Lin Jiu, Satoshi Kuriki, Yi Zhang

    We give an approximate formula for the distribution of the largest eigenvalue of real Wishart matrices by the expected Euler characteristic method for general dimension. The formula is expressed in terms of a definite integral with parameters. We derive a differential equation satisfied by the integral for the 2×2 matrix case and perform a numerical analysis of it.

    更新日期:2020-06-09
  • Linear orderings of the scale mixtures of the multivariate skew-normal distribution
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-06
    Mehdi Amiri, Salman Izadkhah, Ahad Jamalizadeh

    In this paper, (positive) linear stochastic orderings of random vectors X and Y having scale mixtures of the multivariate skew-normal distribution are studied. Necessary and sufficient convenient conditions for a⊤X to be less than a⊤Y, when a is a vector of positive values, in the sense of usual, convex and increasing convex stochastic orders are grasped. The results are potentially applied to conduct

    更新日期:2020-06-06
  • Uniform joint screening for ultra-high dimensional graphical models
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-06
    Zemin Zheng, Haiyu Shi, Yang Li, Hui Yuan

    Identifying large-scale conditional dependence structures through graphical models is a challenging yet practical problem. Under ultra-high dimensional settings, a screening procedure is generally suggested before variable selection to reduce computational costs. However, most existing screening methods examine the marginal correlations, thus not suitable to discover the conditional dependence in graphical

    更新日期:2020-06-06
  • Scalable interpretable learning for multi-response error-in-variables regression
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-06
    Jie Wu, Zemin Zheng, Yang Li, Yi Zhang

    Corrupted data sets containing noisy or missing observations are prevalent in various contemporary applications such as economics, finance and bioinformatics. Despite the recent methodological and algorithmic advances in high-dimensional multi-response regression, how to achieve scalable and interpretable estimation under contaminated covariates is unclear. In this paper, we develop a new methodology

    更新日期:2020-06-06
  • A procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-06-01
    Shan Luo, Zehua Chen

    In this article, we consider discrimination analyses in high-dimensional cases where the dimension of the predictor vector diverges with the sample size in a theoretical setting. The emphasis is on the case where the number of classes is bigger than two. We first deal with the asymptotic misclassification rates of linear discrimination rules under various conditions. In practical high-dimensional classification

    更新日期:2020-06-01
  • Testing normality of data on a multivariate grid
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-05-28
    Lajos Horváth, Piotr Kokoszka, Shixuan Wang

    We propose a significance test to determine if data on a regular d-dimensional grid can be assumed to be a realization of Gaussian process. By accounting for the spatial dependence of the observations, we derive statistics analogous to sample skewness and kurtosis. We show that the sum of squares of these two statistics converges to a chi-square distribution with two degrees of freedom. This leads

    更新日期:2020-05-28
  • Sparsity-regularized skewness estimation for the multivariate skew normal and multivariate skew t distributions
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-05-27
    Sheng Wang, Dale L. Zimmerman, Patrick Breheny

    The multivariate skew normal (MSN) and multivariate skew t (MST) distributions have received considerable attention in the past two decades because of their appealing mathematical properties and their usefulness for modeling skewed data. We develop sparse regularization methodology for estimating the skewness parameters of these two distributions. This methodology facilitates skewness selection, i

    更新日期:2020-05-27
  • Polynomial traces and elementary symmetric functions in the latent roots of a non-central Wishart matrix
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-05-07
    Elvira Di Nardo

    Hypergeometric functions and zonal polynomials are the tools usually addressed in the literature to deal with the expected value of the elementary symmetric functions in non-central Wishart latent roots. The method here proposed recovers the expected value of these symmetric functions by using the umbral operator applied to the trace of suitable polynomial matrices and their cumulants. The employment

    更新日期:2020-05-07
  • Consistent Bayesian sparsity selection for high-dimensional Gaussian DAG models with multiplicative and beta-mixture priors
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-29
    Xuan Cao, Kshitij Khare, Malay Ghosh

    Estimation of the covariance matrix for high-dimensional multivariate datasets is a challenging and important problem in modern statistics. In this paper, we focus on high-dimensional Gaussian DAG models where sparsity is induced on the Cholesky factor L of the inverse covariance matrix. In recent work, (Cao et al., 2019), we established high-dimensional sparsity selection consistency for a hierarchical

    更新日期:2020-04-29
  • Testing for independence of high-dimensional variables: ρV-coefficient based approach
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-25
    Masashi Hyodo, Takahiro Nishiyama, Tatjana Pavlenko

    We treat the problem of testing mutual independence of k high-dimensional random vectors when the data are multivariate normal and k≥2 is a fixed integer. For this purpose, we focus on the vector correlation coefficient, ρV and propose an extension of its classical estimator which is constructed to correct potential sources of inconsistency related to the high dimensionality. Building on the proposed

    更新日期:2020-04-25
  • Two-way MANOVA with unequal cell sizes and unequal cell covariance matrices in high-dimensional settings
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-24
    Hiroki Watanabe, Masashi Hyodo, Shigekazu Nakagawa

    In this paper, we discuss a two-way multivariate analysis of variance in high-dimensional settings. With a high-dimensional setting, we propose new approximate tests that work well under the following conditions: 1. The error vectors do not necessarily follow a multivariate normal distribution, 2. The cell sizes are unequal, 3. The cell covariance matrices are unequal, and 4. The dimension p is much

    更新日期:2020-04-24
  • Pseudo-quantile functional data clustering
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-21
    Joonpyo Kim, Hee-Seok Oh

    This paper studies the problem of functional data clustering. Functional data have their own characteristics and contain rich information that cannot be obtained when regarding the data as multivariate data. Functional data are inherently infinite-dimensional, so classical clustering techniques for finite-dimensional data may not be suitable for functional data. There are several clustering methods

    更新日期:2020-04-21
  • An independence test based on recurrence rates
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-07
    Juan Kalemkerian, Diego Fernández

    A new test of independence between random elements is presented in this article. The test is based on a functional of the Cramér–von Mises type, which is applied to a U-process that is defined from the recurrence rates. Theorems of asymptotic distribution under H0, and consistency under a wide class of alternatives are obtained. The results under contiguous alternatives are also shown. The test has

    更新日期:2020-04-07
  • Likelihood-based tests for parameter constancy in I(2) CVAR models with an application to fixed-term deposit data
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-06
    Takamitsu Kurita

    This paper explores likelihood-based tests for parameter constancy in I(2) cointegrated vector autoregressive (CVAR) models. A new class of test statistics for parameter stability is introduced in the I(2) CVAR framework. This study proves that their asymptotic distributions take non-standard forms involving the integrals of Brownian motions, but they are free of any nuisance parameters. It is thus

    更新日期:2020-04-06
  • Smallest singular value and limit eigenvalue distribution of a class of non-Hermitian random matrices with statistical application
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-04
    Arup Bose, Walid Hachem

    Suppose X is an N×n complex matrix whose entries are centered, independent, and identically distributed random variables with variance 1∕n and whose fourth moment is of order O(n−2). Suppose A is a deterministic matrix whose smallest and largest singular values are bounded below and above respectively, and z≠0 is a complex number. First we consider the matrix XAX∗−z, and obtain asymptotic probability

    更新日期:2020-04-04
  • Comparison of aggregation, minimum and maximum of two risky portfolios with dependent claims
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-04-02
    Saeed Ariyafar, Mahbanoo Tata, Mohsen Rezapour, Mohsen Madadi

    The comparison of two risky portfolios has always been of interest in insurance and finance. Classically, it is often assumed that the portfolio claims are independent, but in practice, this assumption is not usually true and we need to study portfolios with dependent claims. In this paper, we consider two risky portfolios with dependent claims whose dependencies are modeled using Archimedean copulas

    更新日期:2020-04-02
  • Updating of the Gaussian graphical model through targeted penalized estimation
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-27
    Wessel N. van Wieringen, Koen A. Stam, Carel F.W. Peeters, Mark A. van de Wiel

    Updating of the Gaussian graphical model via shrinkage estimation is studied. This shrinkage is towards a nonzero parameter value representing prior quantitative information. Once new data become available, the previously estimated parameter needs updating. Shrinkage provides the means to this end, using the latter as a shrinkage target to acquire an updated estimate. The process of iteratively updating

    更新日期:2020-03-27
  • Parametrising correlation matrices
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-26
    Peter J. Forrester, Jiyuan Zhang

    Correlation matrices are the sub-class of positive definite real matrices with all entries on the diagonal equal to unity. Earlier work has exhibited a parametrisation of the corresponding Cholesky factorisation in terms of partial correlations, and also in terms of hyperspherical co-ordinates. We show how the two are related, starting from the definition of the partial correlations in terms of the

    更新日期:2020-03-26
  • On Kendall’s regression
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-23
    Alexis Derumigny, Jean-David Fermanian

    Conditional Kendall’s tau is a measure of dependence between two random variables, conditionally on some covariates. We assume a regression-type relationship between conditional Kendall’s tau and some covariates, in a parametric setting with a large number of transformations of a small number of regressors. This model may be sparse, and the underlying parameter is estimated through a penalized criterion

    更新日期:2020-03-23
  • Model-free feature screening for ultrahigh dimensional classification
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-20
    Ying Sheng, Qihua Wang

    In this paper, a new model-free feature screening method based on classification accuracy of marginal classifiers is proposed for ultrahigh dimensional classification. Different from existing methods, which use the differences of means or differences of conditional cumulative distribution functions between classes as the screening indexes, we propose a new feature screening method to rank the importance

    更新日期:2020-03-20
  • Joint and marginal causal effects for binary non-independent outcomes
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-14
    Monia Lupparelli, Alessandra Mattei

    Causal inference on multiple non-independent outcomes raises serious challenges, because multivariate techniques that properly account for the outcome’s dependence structure need to be considered. We focus on the case of binary outcomes framing our discussion in the potential outcome approach to causal inference. We define causal effects of treatment on joint outcomes introducing the notion of product

    更新日期:2020-03-14
  • Trinity tests of functions for conditional moment models
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-14
    Jing Tao

    This paper considers conditional moment models where the parameters of interest include both finite-dimensional parameters and unknown functions. We propose sup-Wald, sup-quasi-likelihood ratio and sup-Lagrange multiplier statistics for testing functionals restrictions uniformly over the support for both finite and infinite dimensional components of the parameters. The trinity of three statistics holds

    更新日期:2020-03-14
  • Ridge-type linear shrinkage estimation of the mean matrix of a high-dimensional normal distribution
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-12
    Ryota Yuasa, Tatsuya Kubokawa

    The estimation of the mean matrix of the multivariate normal distribution is addressed in the high dimensional setting. Efron–Morris-type linear shrinkage estimators with ridge modification for the precision matrix instead of the Moore–Penrose generalized inverse are considered, and the weights in the ridge-type linear shrinkage estimators are estimated in terms of minimizing the Stein unbiased risk

    更新日期:2020-03-12
  • Robust nonparametric estimation of the conditional tail dependence coefficient
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-09
    Yuri Goegebeur, Armelle Guillou, Nguyen Khanh Le Ho, Jing Qin

    We consider robust and nonparametric estimation of the coefficient of tail dependence in presence of random covariates. The estimator is obtained by fitting the extended Pareto distribution locally to properly transformed bivariate observations using the minimum density power divergence criterion. We establish convergence in probability and asymptotic normality of the proposed estimator under some

    更新日期:2020-03-09
  • Likelihood ratio tests for many groups in high dimensions
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-07
    Holger Dette, Nina Dörnemann

    In this paper, we investigate the asymptotic distribution of likelihood ratio tests in models with several groups, when the number of groups converges with the dimension and sample size to infinity. We derive central limit theorems for the logarithm of various test statistics and compare our results with the approximations obtained from a central limit theorem where the number of groups is fixed.

    更新日期:2020-03-07
  • An objective prior for hyperparameters in normal hierarchical models
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-07
    James O. Berger, Dongchu Sun, Chengyuan Song

    Hierarchical models are the workhorse of much of Bayesian analysis, yet there is uncertainty as to which priors to use for hyperparameters. Formal approaches to objective Bayesian analysis, such as the Jeffreys-rule approach or reference prior approach, are only implementable in simple hierarchical settings. It is thus common to use less formal approaches, such as utilizing formal priors from non-hierarchical

    更新日期:2020-03-07
  • A Central Limit Theorem for extrinsic antimeans and estimation of Veronese–Whitney means and antimeans on planar Kendall shape spaces
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-03-03
    Yunfan Wang, Vic Patrangenaru, Ruite Guo

    This article is concerned with random objects in the complex projective space ℂPk−2. It is shown that the Veronese–Whitney (VW) antimean, which is the extrinsic antimean of a random point on ℂPk−2 relative to the VW-embedding, is given by the point on ℂPk−2 represented by the eigenvector corresponding to the smallest eigenvalue of the expected mean of the VW-embedding of the random point, provided

    更新日期:2020-03-03
  • A goodness-of-fit test for elliptical distributions with diagnostic capabilities
    J. Multivar. Anal. (IF 1.136) Pub Date : 2020-02-29
    Gilles R. Ducharme, Pierre Lafaye de Micheaux

    This paper develops a test of goodness-of-fit for elliptical distributions. The test is invariant to affine-linear transformations and has a convenient expression that can be broken into components containing diagnostic information to be used to identify possible departures when the test rejects. The test is developed for the bivariate Laplace, logistic and Pearson type II distributions, as well as

    更新日期:2020-02-29
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
Springer 纳米技术权威期刊征稿
全球视野覆盖
施普林格·自然新
chemistry
3分钟学术视频演讲大赛
物理学研究前沿热点精选期刊推荐
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
ACS Publications填问卷
阿拉丁试剂right
麻省大学
西北大学
湖南大学
华东师范大学
王要兵
化学所
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
胡眆昊
杨财广
廖矿标
试剂库存
down
wechat
bug