185 citations · 323 across the 15 of their papers we have counts for
7 papers · 1 filter
Determining the number of factors in a large-dimensional generalised factor model
Rui Wang, Dandan Jiang
This paper proposes new estimators of the number of factors for a generalised factor model with more relaxed assumptions than the strict factor model. Under the framework of large…
A universal test on spikes in a high-dimensional generalized spiked model and its applications
Dandan Jiang
This paper aims to test the number of spikes in a generalized spiked covariance matrix, the spiked eigenvalues of which may be extremely larger or smaller than the non-spiked ones.…
A New Test of Multivariate Nonlinear Causality
Zhidong Bai, Yongchang Hui, Zhihui Lv +3
The multivariate nonlinear Granger causality developed by Bai et al. (2010) plays an important role in detecting the dynamic interrelationships between two groups of variables. Fol…
Modifications of Wald's score tests on large dimensional covariance matrices structure
Dandan Jiang, QiBin Zhang
This paper considers testing the covariance matrices structure based on Wald's score test in large dimensional setting. The hypothesis for a given matrix , which…
Likelihood-based tests on linear hypotheses of large dimensional mean vectors with unequal covariance matrices
Dandan Jiang
This paper considers testing linear hypotheses of a set of mean vectors with unequal covariance matrices in large dimensional setting. The problem of testing the hypothesis $H_0 :…
Tests for Large Dimensional Covariance Structure Based on Rao's Score Test
Dandan Jiang
This paper proposes a new test for covariance matrices structure based on the correction to Rao's score test in large dimensional framework. By generalizing the CLT for the linear…
Testing linear hypotheses in high-dimensional regressions
Z. Bai, D. Jiang, J. Yao +1
For a multivariate linear model, Wilk's likelihood ratio test (LRT) constitutes one of the cornerstone tools. However, the computation of its quantiles under the null or the altern…