Independence Test for High Dimensional Random Vectors

I tiakina i:

Ngā taipitopito rārangi puna kōrero
I whakaputaina i:	arXiv.org (May 30, 2012), p. n/a
Kaituhi matua:	Pan, G M
Ētahi atu kaituhi:	Gao, J, Yang, Y, Guo, M
I whakaputaina:	Cornell University Library, arXiv.org
Ngā marau:	New York Stock Exchange > NYSE Estimating techniques Covariance matrix Spatial data Arches Stock exchanges Characteristic functions Dependence Computer simulation Economic models
Urunga tuihono:	Citation/Abstract Full text outside of ProQuest
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Whakaahuatanga
Whakarāpopotonga:	This paper proposes a new mutual independence test for a large number of high dimensional random vectors. The test statistic is based on the characteristic function of the empirical spectral distribution of the sample covariance matrix. The asymptotic distributions of the test statistic under the null and local alternative hypotheses are established as dimensionality and the sample size of the data are comparable. We apply this test to examine multiple MA(1) and AR(1) models, panel data models with some spatial cross-sectional structures. In addition, in a flexible applied fashion, the proposed test can capture some dependent but uncorrelated structures, for example, nonlinear MA(1) models, multiple ARCH(1) models and vandermonde matrices. Simulation results are provided for detecting these dependent structures. An empirical study of dependence between closed stock prices of several companies from New York Stock Exchange (NYSE) demonstrates that the feature of cross--sectional dependence is popular in stock markets.
ISSN:	2331-8422
Puna:	Engineering Database