Asymptotic normality of quadratic forms with random vectors of increasing dimension
DOI10.1016/J.JMVA.2017.11.002zbMath1380.62083DBLPjournals/ma/PengS18OpenAlexW2770628183WikidataQ64371725 ScholiaQ64371725MaRDI QIDQ1686240
Publication date: 21 December 2017
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/1805/14907
empirical likelihoodLindeberg conditionmartingale central limit theoremchi-square test with increasing number of cellsequal marginalsindependence of components of high-dimensional normal random vectors
Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15)
Related Items (2)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Empirical likelihood ratio confidence regions
- Extending the scope of empirical likelihood
- Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity
- On the asymptotic distribution of quadratic forms in uniform order statistics
- Limit theorems for polylinear forms
- Asymptotic normality of quadratic forms of martingale differences
- High dimensional generalized empirical likelihood for moment restrictions with dependent data
- Uniform convergence of convolution estimators for the response density in nonparametric regression
- Efficient estimation of linear functionals of a bivariate distribution with equal, but unknown marginals: the least-squares approach
- Effects of data dimension on empirical likelihood
- Testing for complete independence in high dimensions
- Empirical likelihood ratio confidence intervals for a single functional
- Some Limit Theorems for Polynomials of Second Degree
- On the Distribution of a Quadratic Form in Many Random Variables
- Tests for High-Dimensional Covariance Matrices
This page was built for publication: Asymptotic normality of quadratic forms with random vectors of increasing dimension
