A geometric investigation into the tail dependence of vine copulas
Vine copulas are a type of multivariate dependence model, composed of a collection of bivariate copulas that are combined according to a specific underlying graphical structure. Their flexibility and practicality in moderate and high ...
Depth-based classification for relational data with multiple attributes
With the recent progress of data acquisition technology, classification of data exhibiting relational dependence, from online social interactions to multi-omics studies to linkage of electronic health records, continues to gain an ever ...
Projection theorems and estimating equations for power-law models
We extend projection theorems concerning Hellinger and Jones et al. divergences to the continuous case. These projection theorems reduce certain estimation problems on generalized exponential models to linear problems. We introduce the ...
A topologically valid construction of depth for functional data
Numerous problems remain in the construction of statistical depth for functional data. Issues stem largely from the absence of a well-conceived notion of symmetry. The present paper proposes a topologically valid notion of symmetry for ...
Heckman selection-t model: Parameter estimation via the EM-algorithm
The Heckman selection model is perhaps the most popular econometric model in the analysis of data with sample selection. The analyses of this model are based on the normality assumption for the error terms, however, in some ...
An association test for functional data based on Kendall’s Tau
We consider the problem of testing for association between a functional variable belonging to a Hilbert space and a scalar variable. Particularly, we propose a distribution-free test statistic based on Kendall’s Tau, which is a popular ...
A new general class of RC association models: Estimation and main properties
The paper introduces a new class of row-column (RC) association models for contingency tables by allowing the user to select both the scale on which interactions are measured as in Kateri and Papaioannou (1994) and the type of logit (...
Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix
The estimation of structured covariance matrix arises in many fields. An appropriate covariance structure not only improves the accuracy of covariance estimation but also increases the efficiency of mean parameter estimators in ...
Distance-based tests for planar shape
Statistical shape analysis (SSA) may be understood like a field of Multivariate Analysis and has assumed a prominent position due to its applicability in various areas; such as in imagery processing, biology, anatomy, among others. ...
Limiting laws for extreme eigenvalues of large-dimensional spiked Fisher matrices with a divergent number of spikes
Consider the p × p matrix that is the product of a population covariance matrix and the inverse of another population covariance matrix. Suppose that their difference has a divergent rank with respect to p, when two samples of sizes n ...
Minimum Average Variance Estimation with group Lasso for the multivariate response Central Mean Subspace
The Minimum Average Variance Estimation (MAVE) method and its variants have proven to be effective approaches to the dimension reduction problems. However, as far as we know, using MAVE to estimate the Central Mean Subspace (CMS) with ...
On classification with nonignorable missing data
We consider the problem of kernel classification with nonignorable missing data. Instead of imposing a fully parametric model for the selection probability, which can be quite sensitive to the violations of model assumptions, here we ...
Kick-one-out-based variable selection method for Euclidean distance-based classifier in high-dimensional settings
This paper presents a variable selection method for the Euclidean distance-based classifier in high-dimensional settings. We are concerned that the expected probabilities of misclassification (EPMC) for the Euclidean distance-based ...
Principal loading analysis
This paper proposes a tool for dimension reduction where the dimension of the original space is reduced: the principal loading analysis. Principal loading analysis is a tool to reduce dimensions by discarding variables. The intuition ...
Expectile depth: Theory and computation for bivariate datasets
Expectiles are the solution to an asymmetric least squares minimization problem for univariate data. They resemble the quantiles, and just like them, expectiles are indexed by a level α in the unit interval. In the present paper, we ...
Stochastic decomposition for ℓ p-norm symmetric survival functions on the positive orthant
We derive a stochastic representation for the probability distribution on the positive orthant ( 0 , ∞ ) d whose association between components is minimal among all probability laws with ℓ p-norm symmetric survival functions. It is ...
On the asymptotic normality and efficiency of Kronecker envelope principal component analysis
Dimension reduction methods for matrix or tensor data have been an active research field in recent years. Li et al. (2010) introduced the notion of the Kronecker envelope and proposed dimension folding estimators for supervised ...
A high dimensional nonparametric test for proportional covariance matrices
This work is concerned with testing the proportionality between two high dimensional covariance matrices. Several tests for proportional covariance matrices, based on modifying the classical likelihood ratio test and applicable in high ...
Semi-parametric estimation of multivariate extreme expectiles
This paper focuses on semi-parametric estimation of multivariate expectiles for extreme levels of risk. Multivariate expectiles and their extremes have been the focus of plentiful research in recent years. In particular, it has been ...
Inference in high dimensional linear measurement error models
For a high dimensional linear model with a finite number of covariates measured with errors, we study statistical inference on the parameters associated with the error-prone covariates, and propose a new corrected decorrelated score ...