Sparse functional varying-coefficient mixture regression
The functional varying-coefficient model (FVCM) provides a simple yet efficient method for function on scalar regression. However, classical FVCM typically assumes that varying associations between functional responses and scalar covariates are ...
A general approach for testing independence in Hilbert spaces
We generalize the projection correlation idea for testing independence of random vectors which is known as a powerful method in multivariate analysis. A universal Hilbert space approach makes the new testing procedures useful in various cases and ...
Multivariate robust linear models for multivariate longitudinal data
Linear models commonly used in longitudinal data analysis often assume a multivariate normal distribution. This assumption, however, can lead to biased mean parameter estimates in the presence of outliers. To address this, alternative linear ...
New multivariate Gini’s indices
The Gini’s mean difference was defined as the expected absolute difference between a random variable and its independent copy. The corresponding normalized version, namely Gini’s index, denotes two times the area between the egalitarian line and ...
Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule
We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing. The proposed test ...
Alteration detection of tensor dependence structure via sparsity-exploited reranking algorithm
Tensor-valued data arise frequently from a wide variety of scientific applications, and many among them can be translated into an alteration detection problem of tensor dependence structures. In this article, we formulate the problem under the ...
Equality tests of covariance matrices under a low-dimensional factor structure
We propose an equality test to compare two covariance matrices in a high-dimensional framework while accommodating a low-dimensional latent factor model. We show that null limiting distributions of the test statistics follow a weighted mixture of ...
Efficient estimation of a partially linear panel data model with cross-sectional dependence
This paper considers efficiency improvements in a partially linear panel data model that accounts for possible nonlinear effects of common covariates and allows for cross-sectional dependence arising simultaneously from unobserved common factors ...
