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A consistent test of equality of distributions for Hilbert-valued random elements J. Multivar. Anal. (IF 1.6) Pub Date : 2024-03-15 Gil González–Rodríguez, Ana Colubi, Wenceslao González–Manteiga, Manuel Febrero–Bande
Two independent random elements taking values in a separable Hilbert space are considered. The aim is to develop a test with bootstrap calibration to check whether they have the same distribution or not. A transformation of both random elements into a new separable Hilbert space is considered so that the equality of expectations of the transformed random elements is equivalent to the equality of distributions
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Change point analysis of functional variance function with stationary error J. Multivar. Anal. (IF 1.6) Pub Date : 2024-03-11 Qirui Hu
An asymptotically correct test for an abrupt break in functional variance function of measurement error in the functional sequence and the confidence interval of change point is constructed. Under general assumptions, the test and detection procedure conducted by Spline-backfitted kernel smoothing, i.e., recovering trajectories with B-spline and estimating variance function with kernel regression,
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On heavy-tailed risks under Gaussian copula: The effects of marginal transformation J. Multivar. Anal. (IF 1.6) Pub Date : 2024-03-01 Bikramjit Das, Vicky Fasen-Hartmann
In this paper, we compute multivariate tail risk probabilities where the marginal risks are heavy-tailed and the dependence structure is a Gaussian copula. The marginal heavy-tailed risks are modeled using regular variation which leads to a few interesting consequences. First, as the threshold increases, we note that the rate of decay of probabilities of tail sets varies depending on the type of tail
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High-dimensional nonconvex LASSO-type M-estimators J. Multivar. Anal. (IF 1.6) Pub Date : 2024-02-27 Jad Beyhum, François Portier
A theory is developed to examine the convergence properties of -norm penalized high-dimensional -estimators, with nonconvex risk and unrestricted domain. Under high-level conditions, the estimators are shown to attain the rate of convergence , where is the number of nonzero coefficients of the parameter of interest. Sufficient conditions for our main assumptions are then developed and finally used
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Nonlinear sufficient dimension reduction for distribution-on-distribution regression J. Multivar. Anal. (IF 1.6) Pub Date : 2024-02-27 Qi Zhang, Bing Li, Lingzhou Xue
We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional data, modeled as members of a metric space. Our key step is to build universal kernels (cc-universal) on the metric spaces, which results in reproducing kernel Hilbert spaces for the predictor and response that are rich enough to characterize the conditional
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Linearized maximum rank correlation estimation when covariates are functional J. Multivar. Anal. (IF 1.6) Pub Date : 2024-02-24 Wenchao Xu, Xinyu Zhang, Hua Liang
This paper extends the linearized maximum rank correlation (LMRC) estimation proposed by Shen et al. (2023) to the setting where the covariate is a function. However, this extension is nontrivial due to the difficulty of inverting the covariance operator, which may raise the ill-posed inverse problem, for which we integrate the functional principal component analysis to the LMRC procedure. The proposed
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Latent model extreme value index estimation J. Multivar. Anal. (IF 1.6) Pub Date : 2024-02-15 Joni Virta, Niko Lietzén, Lauri Viitasaari, Pauliina Ilmonen
We propose a novel strategy for multivariate extreme value index estimation. In applications such as finance, volatility and risk of multivariate time series are often driven by the same underlying factors. To estimate the latent risks, we apply a two-stage procedure. First, a set of independent latent series is estimated using a method of latent variable analysis. Then, univariate risk measures are
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Estimation of multiple networks with common structures in heterogeneous subgroups J. Multivar. Anal. (IF 1.6) Pub Date : 2024-02-13 Xing Qin, Jianhua Hu, Shuangge Ma, Mengyun Wu
Network estimation has been a critical component of high-dimensional data analysis and can provide an understanding of the underlying complex dependence structures. Among the existing studies, Gaussian graphical models have been highly popular. However, they still have limitations due to the homogeneous distribution assumption and the fact that they are only applicable to small-scale data. For example
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A data depth based nonparametric test of independence between two random vectors J. Multivar. Anal. (IF 1.6) Pub Date : 2024-02-08 Sakineh Dehghan, Mohammad Reza Faridrohani
A new family of depth-based test statistics is proposed for testing the hypothesis of independence between two random vectors. In the procedure to derive the asymptotic distribution of the tests under the null hypothesis, we do not require any symmetric assumption of the distribution functions. Furthermore, a conditional distribution-free property of the tests is shown. The asymptotic relative efficiency
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Convergence analysis of data augmentation algorithms for Bayesian robust multivariate linear regression with incomplete data J. Multivar. Anal. (IF 1.6) Pub Date : 2024-01-20 Haoxiang Li, Qian Qin, Galin L. Jones
Gaussian mixtures are commonly used for modeling heavy-tailed error distributions in robust linear regression. Combining the likelihood of a multivariate robust linear regression model with a standard improper prior distribution yields an analytically intractable posterior distribution that can be sampled using a data augmentation algorithm. When the response matrix has missing entries, there are unique
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On positive association of absolute-valued and squared multivariate Gaussians beyond MTP2 J. Multivar. Anal. (IF 1.6) Pub Date : 2024-01-13 Helmut Finner, Markus Roters
We show that positively associated squared (and absolute-valued) multivariate normally distributed random vectors need not be multivariate totally positive of order 2 (MTP2) for p≥3. This result disproves Theorem 1 in Eisenbaum (2014, Ann. Probab.) and the conjecture that positive association of squared multivariate normals is equivalent to MTP2 and infinite divisibility of squared multivariate normals
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Estimation of sparse covariance matrix via non-convex regularization J. Multivar. Anal. (IF 1.6) Pub Date : 2024-01-05 Xin Wang, Lingchen Kong, Liqun Wang
Estimation of high-dimensional sparse covariance matrix is one of the fundamental and important problems in multivariate analysis and has a wide range of applications in many fields. This paper presents a novel method for sparse covariance matrix estimation via solving a non-convex regularization optimization problem. We establish the asymptotic properties of the proposed estimator and develop a multi-stage
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Hypothesis testing for mean vector and covariance matrix of multi-populations under a two-step monotone incomplete sample in large sample and dimension J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-28 Shin-ichi Tsukada
In this study, we focus on the critical issue of analyzing data sets with missing data. Statistically processing such data sets, particularly those with general missing data, is challenging to express in explicit formulae, and often requires computational algorithms to solve. We specifically address monotone missing data, which are the simplest form of data sets with missing data. We conduct hypothesis
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Estimation of extreme multivariate expectiles with functional covariates J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-23 Elena Di Bernardino, Thomas Laloë, Cambyse Pakzad
The present article is devoted to the semi-parametric estimation of multivariate expectiles for extreme levels. The considered multivariate risk measures also include the possible conditioning with respect to a functional covariate, belonging to an infinite-dimensional space. By using the first order optimality condition, we interpret these expectiles as solutions of a multidimensional nonlinear optimum
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An independence test for functional variables based on kernel normalized cross-covariance operator J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-22 Terence Kevin Manfoumbi Djonguet, Guy Martial Nkiet
We propose an independence test for random variables valued into metric spaces by using a test statistic obtained from appropriately centering and rescaling the squared Hilbert–Schmidt norm of the usual empirical estimator of normalized cross-covariance operator. We then get asymptotic normality of this statistic under independence hypothesis, so leading to a new test for independence of functional
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Shrinkage estimators of BLUE for time series regression models J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-15 Yujie Xue, Masanobu Taniguchi, Tong Liu
The least squares estimator (LSE) seems a natural estimator of linear regression models. Whereas, if the dimension of the vector of regression coefficients is greater than 1 and the residuals are dependent, the best linear unbiased estimator (BLUE), which includes the information of the covariance matrix Γ of residual process has a better performance than LSE in the sense of mean square error. As we
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Asymptotic normality of the local linear estimator of the functional expectile regression J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-05 Ouahiba Litimein, Ali Laksaci, Larbi Ait-Hennani, Boubaker Mechab, Mustapha Rachdi
We are concerned with the nonparametric estimation of the expectile functional regression. More precisely, we build an estimator, by the local linear smoothing approach, of the conditional expectile. Then we establish the asymptotic distribution of the constructed estimator. Establishing this result requires the Bahadur representation of the conditional expectile. The latter is obtained under certain
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Preface to the Special Issue “Copula modeling from Abe Sklar to the present day” J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-02 Christian Genest, Ostap Okhrin, Taras Bodnar
Abstract not available
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A flexible Clayton-like spatial copula with application to bounded support data J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-01 Moreno Bevilacqua, Eloy Alvarado, Christian Caamaño-Carrillo
The Gaussian copula is a powerful tool that has been widely used to model spatial and/or temporal correlated data with arbitrary marginal distributions. However, this kind of model can potentially be too restrictive since it expresses a reflection symmetric dependence. In this paper, we propose a new spatial copula model that makes it possible to obtain random fields with arbitrary marginal distributions
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A proper scoring rule for minimum information bivariate copulas J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-01 Yici Chen, Tomonari Sei
Two-dimensional distributions whose marginal distributions are uniform are called bivariate copulas. Among them, the one that satisfies given constraints on expectation and is closest to being an independent distribution in the sense of Kullback–Leibler divergence is called the minimum information bivariate copula. The density function of the minimum information copula contains a set of functions called
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The weighted characteristic function of the multivariate PIT: Independence and goodness-of-fit tests J. Multivar. Anal. (IF 1.6) Pub Date : 2023-12-01 Jean-François Quessy, Samuel Lemaire-Paquette
Many authors have exploited the fact that the distribution of the multivariate probability integral transformation (PIT) of a continuous random vector with cumulative distribution function is free of the marginal distributions. While most of these methods are based on the cdf of , this paper introduces the weighted characteristic function (WCf) of . A sample version of the WCf of based on pseudo-observations
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Quantifying directed dependence via dimension reduction J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-30 S, e, b, a, s, t, i, a, n, , F, u, c, h, s
Studying the multivariate extension of copula correlation yields a dimension reduction principle, which turns out to be strongly related with the ‘simple measure of conditional dependence’ recently introduced by Azadkia and Chatterjee (2021). In the present paper, we identify and investigate the dependence structure underlying this dimension reduction principle, provide a strongly consistent estimator
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Supermodular and directionally convex comparison results for general factor models J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-30 Jonathan Ansari, Ludger Rüschendorf
This paper provides comparison results for general factor models with respect to the supermodular and directionally convex order. These results extend and strengthen previous ordering results from the literature concerning certain classes of mixture models as mixtures of multivariate normals, multivariate elliptic and exchangeable models to general factor models. For the main results, we first strengthen
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High-dimensional Bernstein–von Mises theorem for the Diaconis–Ylvisaker prior J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-30 Xin Jin, Anirban Bhattacharya, Riddhi Pratim Ghosh
We study the asymptotic normality of the posterior distribution of canonical parameter in the exponential family under the Diaconis–Ylvisaker prior which is a conjugate prior when the dimension of parameter space increases with the sample size. We prove under mild conditions on the true parameter value θ0 and hyperparameters of priors, the difference between the posterior distribution and a normal
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A new method to construct high-dimensional copulas with Bernoulli and Coxian-2 distributions J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-30 Christopher Blier-Wong, Hélène Cossette, Sebastien Legros, Etienne Marceau
We propose an approach to construct a new family of generalized Farlie–Gumbel–Morgenstern (GFGM) copulas that naturally scales to high dimensions. A GFGM copula can model moderate positive and negative dependence, cover different types of asymmetries, and admits exact expressions for many quantities of interest such as measures of association or risk measures in actuarial science or quantitative risk
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Testing homogeneity in high dimensional data through random projections J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-27 Tao Qiu, Qintong Zhang, Yuanyuan Fang, Wangli Xu
Testing for homogeneity of two random vectors is a fundamental problem in statistics. In the past two decades, numerous efforts have been made to detect heterogeneity when the random vectors are multivariate or even high dimensional. Due to the “curse of dimensionality”, existing tests based on Euclidean distance may fail to capture the overall homogeneity in high-dimensional settings while can only
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Penalized estimation of hierarchical Archimedean copula J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-29 Ostap Okhrin, Alexander Ristig
This manuscript discusses a novel estimation approach for parametric hierarchical Archimedean copula. The parameters and structure of this copula are simultaneously estimated while imposing a non-concave penalty on differences between parameters which coincides with an implicit penalty on the copula’s structure. The asymptotic properties of the resulting penalized estimator are studied and small sample
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Copula modeling from Abe Sklar to the present day J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-29 Christian Genest, Ostap Okhrin, Taras Bodnar
This paper provides a structured overview of the contents of the Special Issue of the on “Copula modeling from Abe Sklar to the present day,” along with a brief history of the development of the field.
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[formula omitted]-norm spherical copulas J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-29 Carole Bernard, Alfred Müller, Marco Oesting
In this paper we study -norm spherical copulas for arbitrary and arbitrary dimensions. The study is motivated by a conjecture that these distributions lead to a sharp bound for the value of a certain generalized mean difference. We fully characterize conditions for existence and uniqueness of -norm spherical copulas. Explicit formulas for their densities and correlation coefficients are derived and
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fastMI: A fast and consistent copula-based nonparametric estimator of mutual information J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-29 Soumik Purkayastha, Peter X.-K. Song
As a fundamental concept in information theory, mutual information () has been commonly applied to quantify association between random vectors. Most existing nonparametric estimators of have unstable statistical performance since they involve parameter tuning. We develop a consistent and powerful estimator, called , that does not incur any parameter tuning. Based on a copula formulation, estimates
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A multivariate skew-normal-Tukey-h distribution J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-28 Sagnik Mondal, Marc G. Genton
We introduce a new family of multivariate distributions by taking the component-wise Tukey-h transformation of a random vector following a skew-normal distribution with an alternative parameterization. The proposed distribution is named the skew-normal-Tukey-h distribution and is an extension of the skew-normal distribution for handling heavy-tailed data. We compare this proposed distribution to the
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A novel positive dependence property and its impact on a popular class of concordance measures J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-23 Sebastian Fuchs, Marco Tschimpke
A novel positive dependence property is introduced, called positive measure inducing (PMI for short), being fulfilled by numerous copula classes, including Gaussian, Student t, Fréchet, Farlie–Gumbel–Morgenstern and Frank copulas; it is conjectured that even all positive quadrant dependent Archimedean copulas meet this property. From a geometric viewpoint, a PMI copula concentrates more mass near the
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High-dimensional factor copula models with estimation of latent variables J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-25 Xinyao Fan, Harry Joe
Factor models are a parsimonious way to explain the dependence of variables using several latent variables. In Gaussian 1-factor and structural factor models (such as bi-factor and oblique factor) and their factor copula counterparts, factor scores or proxies are defined as conditional expectations of latent variables given the observed variables. With mild assumptions, the proxies are consistent for
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On convergence and singularity of conditional copulas of multivariate Archimedean copulas, and conditional dependence J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-24 T, h, i, m, o, , M, ., , K, a, s, p, e, r
The present paper derives an explicit expression for (a version of) every uni- and multivariate conditional distribution (i.e., Markov kernel) of Archimedean copulas and uses this representation to generalize a recently established result, saying that in the class of multivariate Archimedean copulas standard uniform convergence implies weak convergence of almost all univariate Markov kernels, to arbitrary
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A class of smooth, possibly data-adaptive nonparametric copula estimators containing the empirical beta copula J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-24 Ivan Kojadinovic, Bingqing Yi
A broad class of smooth, possibly data-adaptive nonparametric copula estimators that contains empirical Bernstein copulas introduced by Sancetta and Satchell (and thus the empirical beta copula proposed by Segers, Sibuya and Tsukahara) is studied. Within this class, a subclass of estimators that depend on a scalar parameter determining the amount of marginal smoothing and a functional parameter controlling
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Multivariate tail dependence and local stochastic dominance J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-24 Karl Friedrich Siburg, Christopher Strothmann
Given two multivariate copulas with corresponding tail dependence functions, we investigate the relation between a natural tail dependence ordering and the order of local stochastic dominance. We show that, although the two orderings are not equivalent in general, they coincide for various important classes of copulas, among them all multivariate Archimedean and bivariate lower extreme value copulas
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Tests of independence and randomness for arbitrary data using copula-based covariances J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-24 Bouchra R. Nasri, Bruno N. Rémillard
In this article, we study tests of independence for data with arbitrary distributions in the non-serial case, i.e., for independent and identically distributed random vectors, as well as in the serial case, i.e., for time series. These tests are derived from copula-based covariances and their multivariate extensions using Möbius transforms. We find the asymptotic distributions of these statistics under
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Copula-based conditional tail indices J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-24 Vincenzo Coia, Harry Joe, Natalia Nolde
Consider a multivariate distribution of , where is a vector of predictor variables and is a response variable. Results are obtained for comparing the conditional and marginal tail indices, and , based on conditional distributions and marginal distribution , respectively. For a multivariate distribution based on a copula, the conditional tail index can be decomposed into a product of copula-based conditional
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Comparison of correlation-based measures of concordance in terms of asymptotic variance J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-24 Takaaki Koike, Marius Hofert
We compare measures of concordance that arise as Pearson’s linear correlation coefficient between two random variables transformed so that they follow the so-called concordance-inducing distributions. The class of such transformed rank correlations includes Spearman’s rho, Blomqvist’s beta and van der Waerden’s coefficient. When only the standard axioms of measures of concordance are required, it is
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A single risk approach to the semiparametric competing risks model with parametric Archimedean risk dependence J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-24 Simon M.S. Lo, Ralf A. Wilke
This paper considers a dependent competing risks model with the distribution of one risk being a semiparametric proportional hazards model, whereas the model for the other risks and the degree of risk dependence of an Archimedean copula are unknown. Identifiability is shown when there is at least one covariate with at least two values. Estimation is done by means of a -consistent semiparametric two-step
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Matrix-valued isotropic covariance functions with local extrema J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-18 Alfredo Alegría, Xavier Emery
Multivariate random fields are commonly used in spatial statistics and natural science to model coregionalized variables. In this context, the matrix-valued covariance function plays a central role in capturing their spatial continuity and interdependence. This study aims to contribute to the literature on covariance modeling by proposing new parametric families of isotropic matrix-valued functions
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Asymptotic properties of hierarchical clustering in high-dimensional settings J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-14 Kento Egashira, Kazuyoshi Yata, Makoto Aoshima
In this study, three asymptotic behaviors of hierarchical clustering are defined and studied with strict conditions under several asymptotic settings, from large samples to high dimensionality, when having two independent populations. We proceed with the current comprehension of the asymptotic properties of hierarchical clustering in high-dimensional, low-sample-size (HDLSS) settings. For high-dimensional
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Statistical performance of quantile tensor regression with convex regularization J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-14 Wenqi Lu, Zhongyi Zhu, Rui Li, Heng Lian
In this paper, we consider high-dimensional quantile tensor regression using a general convex decomposable regularizer and analyze the statistical performances of the estimator. The rates are stated in terms of the intrinsic dimension of the estimation problem, which is, roughly speaking, the dimension of the smallest subspace that contains the true coefficient. Previously, convex regularized tensor
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Non-asymptotic robustness analysis of regression depth median J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-04 Yijun Zuo
The maximum depth estimator (aka depth median) (βRD∗) induced from regression depth (RD) of Rousseeuw and Hubert (1999) is one of the most prevailing estimators in regression. It possesses outstanding robustness similar to the univariate location counterpart. Indeed, βRD∗ can, asymptotically, resist up to 33% contamination without breakdown, in contrast to the 0% for the traditional (least squares
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On moments of truncated multivariate normal/independent distributions J. Multivar. Anal. (IF 1.6) Pub Date : 2023-11-02 Tsung-I Lin, Wan-Lun Wang
Multivariate normal/independent (MNI) distributions contain many renowned heavy-tailed distributions such as the multivariate t, multivariate slash, multivariate contaminated normal, multivariate variance-gamma, and multivariate double exponential distributions. A frequent problem encountered in statistical analysis is the occurrence of truncated observations and non-normality such that theoretical
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Quantile-based MANOVA: A new tool for inferring multivariate data in factorial designs J. Multivar. Anal. (IF 1.6) Pub Date : 2023-10-27 Marléne Baumeister, Marc Ditzhaus, Markus Pauly
Multivariate analysis-of-variance (MANOVA) is a well established tool to examine multivariate endpoints. While classical approaches depend on restrictive assumptions like normality and homogeneity, there is a recent trend to more general and flexible procedures. In this paper, we proceed on this path, but do not follow the typical mean-focused perspective. Instead we consider general quantiles, in
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Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices J. Multivar. Anal. (IF 1.6) Pub Date : 2023-10-21 Ansgar Steland
This paper studies a flexible approach to analyze high-dimensional nonlinear time series of unconstrained dimension based on linear statistics calculated from spectral average statistics of bilinear forms and nonlinear transformations of lag-window (i.e. band-regularized) spectral density matrix estimators. That class of statistics includes, among others, smoothed periodograms, nonlinear statistics
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Large factor model estimation by nuclear norm plus ℓ1 norm penalization J. Multivar. Anal. (IF 1.6) Pub Date : 2023-10-19 Matteo Farnè, Angela Montanari
This paper provides a comprehensive estimation framework via nuclear norm plus ℓ1 norm penalization for high-dimensional approximate factor models with a sparse residual covariance. The underlying assumptions allow for non-pervasive latent eigenvalues and a prominent residual covariance pattern. In that context, existing approaches based on principal components may lead to misestimate the latent rank
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Tests for equality of several covariance matrix functions for multivariate functional data J. Multivar. Anal. (IF 1.6) Pub Date : 2023-10-06 Zhiping Qiu, Jiangyuan Fan, Jin-Ting Zhang, Jianwei Chen
Multivariate functional data are often observed in many scientific fields. This paper considers a multi-sample equal-covariance matrix function testing problem for multivariate functional data. Two new tests are proposed and studied. The asymptotic properties of the two tests under the null hypothesis and a local alternative are investigated. Two methods for approximating the null distributions of
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The skewness of mean–variance normal mixtures J. Multivar. Anal. (IF 1.6) Pub Date : 2023-09-30 Nicola Loperfido
Mean–variance mixtures of normal distributions are very flexible: they model many nonnormal features, such as skewness, kurtosis and multimodality. Special cases include generalized asymmetric Laplace distributions, mixtures of two normal distributions with proportional covariance matrices, scale mixtures of normal distributions and normal distributions. This paper investigates the skewness of multivariate
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Test of conditional independence in factor models via Hilbert–Schmidt independence criterion J. Multivar. Anal. (IF 1.6) Pub Date : 2023-09-23 Kai Xu, Qing Cheng
This work is concerned with testing conditional independence under a factor model setting. We propose a novel multivariate test for non-Gaussian data based on the Hilbert–Schmidt independence criterion (HSIC). Theoretically, we investigate the convergence of our test statistic under both the null and the alternative hypotheses, and devise a bootstrap scheme to approximate its null distribution, showing
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Stochastic representations and probabilistic characteristics of multivariate skew-elliptical distributions J. Multivar. Anal. (IF 1.6) Pub Date : 2023-09-20 Chuancun Yin, Narayanaswamy Balakrishnan
The family of multivariate skew-normal distributions has many interesting properties. It is shown here that these hold for a general class of skew-elliptical distributions. For this class, several stochastic representations are established and then their probabilistic properties, such as characteristic function, moments, quadratic forms as well as transformation properties, are investigated.
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Tests for group-specific heterogeneity in high-dimensional factor models J. Multivar. Anal. (IF 1.6) Pub Date : 2023-09-13 Antoine Djogbenou, Razvan Sufana
Standard high-dimensional factor models assume that the comovements in a large set of variables could be modeled using a small number of latent factors that affect all variables. In many relevant applications in economics and finance, heterogeneous comovements specific to some known groups of variables naturally arise, and reflect distinct cyclical movements within those groups. This paper develops
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On testing the equality of latent roots of scatter matrices under ellipticity J. Multivar. Anal. (IF 1.6) Pub Date : 2023-09-09 Gaspard Bernard, Thomas Verdebout
In the present paper, we tackle the problem of testing H0q:λq>λq+1=⋯=λp, where λ1,…,λp are the scatter matrix eigenvalues of an elliptical distribution on Rp. This is a classical problem in multivariate analysis which is very useful in dimension reduction. We analyse the problem using the Le Cam asymptotic theory of experiments and show that contrary to the testing problems on eigenvalues and eigenvectors
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Estimation in nonparametric functional-on-functional models with surrogate responses J. Multivar. Anal. (IF 1.6) Pub Date : 2023-08-12 Mounir Boumahdi, Idir Ouassou, Mustapha Rachdi
We construct an estimator for the regression operator of a functional response variable using surrogate data, given a functional random variable. The almost complete uniform convergence rate of the estimator is then established. Finally, to demonstrate the predictive utility and superiority of the estimator when dealing with incomplete data, we apply the methodology to both simulated data and meteorological
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Uniformly valid inference based on the Lasso in linear mixed models J. Multivar. Anal. (IF 1.6) Pub Date : 2023-08-09 Peter Kramlinger, Ulrike Schneider, Tatyana Krivobokova
Linear mixed models (LMMs) are suitable for clustered data and are common in biometrics, medicine, survey statistics, and many other fields. In those applications, it is essential to carry out valid inference after selecting a subset of the available variables. We construct confidence sets for the fixed effects in Gaussian LMMs that are based on Lasso-type estimators. Aside from providing confidence
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Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space J. Multivar. Anal. (IF 1.6) Pub Date : 2023-08-05 Haihan Xie, Linglong Kong
To relax the linear assumption in function-on-scalar regression, we borrow the strength of copula and propose a novel Gaussian copula function-on-scalar regression. Our model is more flexible to characterize the dynamic relationship between functional response and scalar predictors. Estimation, prediction, and inference are fully investigated. We develop a closed form for the estimator of coefficient
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A nonparametric test for paired data J. Multivar. Anal. (IF 1.6) Pub Date : 2023-08-02 Grzegorz Wyłupek
The paper proposes a weighted Kolmogorov–Smirnov type test for the two-sample problem for paired data. The asymptotic distribution of the test statistic under the null model is derived. The dependence of both the finite sample and the asymptotic distribution of the test statistic on the dependence structure of the data requires the use of the wild bootstrap technique for inference. The related wild
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False discovery rate approach to dynamic change detection J. Multivar. Anal. (IF 1.6) Pub Date : 2023-08-01 Lilun Du, Mengtao Wen
In multiple data stream surveillance, the rapid and sequential identification of individuals whose behavior deviates from the norm has become particularly important. In such applications, the state of a stream can alternate, possibly multiple times, between a null state and an alternative state. To balance the ability to detect two types of changes, that is, a change from the null to the alternative