Tests for group-specific heterogeneity in high-dimensional factor models
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, ...
On testing the equality of latent roots of scatter matrices under ellipticity
In the present paper, we tackle the problem of testing H 0 q : λ q > λ q + 1 = ⋯ = λ p, where λ 1 , … , λ p are the scatter matrix eigenvalues of an elliptical distribution on R p. This is a classical problem in multivariate analysis which is ...
Stochastic representations and probabilistic characteristics of multivariate skew-elliptical distributions
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 ...
Test of conditional independence in factor models via Hilbert–Schmidt independence criterion
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 ...
The skewness of mean–variance normal mixtures
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 ...
Tests for equality of several covariance matrix functions for multivariate functional data
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 ...
Large factor model estimation by nuclear norm plus ℓ 1 norm penalization
This paper provides a comprehensive estimation framework via nuclear norm plus ℓ 1 norm penalization for high-dimensional approximate factor models with a sparse residual covariance. The underlying assumptions allow for non-pervasive latent ...
Highlights
- High-dimensional approximate factor models are recoverable.
- Ordinary least squares penalized by nuclear norm plus l 1 norm is a solution.
- Latent eigenvalues can be intermediately spiked with different magnitudes.
- Non-diagonal ...
Quantile-based MANOVA: A new tool for inferring multivariate data in factorial designs
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 nonlinear inference and change-point testing of high-dimensional spectral density matrices
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-...
On moments of truncated multivariate normal/independent distributions
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 ...
Non-asymptotic robustness analysis of regression depth median
The maximum depth estimator (aka depth median) (β R D ∗) 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 ...
Asymptotic properties of hierarchical clustering in high-dimensional settings
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 ...
