A Nonparametric Test of Serial Independence for Time Series and Residuals

This paper presents nonparametric tests of independence that can be used to test the independence of p random variables, serial independence for time series, or residuals data. These tests are shown to generalize the classical portmanteau statistics. Applications to both time series and regression r...

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Veröffentlicht in:	Journal of multivariate analysis 2001-11, Vol.79 (2), p.191-218
Hauptverfasser:	Ghoudi, Kilani, Kulperger, Reg J., Rémillard, Bruno
Format:	Artikel
Sprache:	eng
Schlagworte:	Cramér–von Mises statistics empirical processes Exact sciences and technology independence independence serial independence empirical processes pseudo-observations residuals weak convergence Cramer-von Mises statistics Inference from stochastic processes time series analysis Mathematics Probability and statistics pseudo-observations residuals Sciences and techniques of general use serial independence Statistics weak convergence
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Beschreibung
Zusammenfassung:	This paper presents nonparametric tests of independence that can be used to test the independence of p random variables, serial independence for time series, or residuals data. These tests are shown to generalize the classical portmanteau statistics. Applications to both time series and regression residuals are discussed.
ISSN:	0047-259X 1095-7243
DOI:	10.1006/jmva.2000.1967