DocumentCode :
3382064
Title :
Testing that a multivariate stationary time-series is Gaussian
Author :
Moulines, E. ; Choukri, K. ; Sharbit, M.
Author_Institution :
Telecom-Paris, France
fYear :
1992
fDate :
7-9 Oct 1992
Firstpage :
185
Lastpage :
188
Abstract :
These tests are based on quadratic form in deviations of certain sample statistics from their ensemble counterpart, minimised with respect to the unknown parameters. They are shown to converge under the null hypothesis to a chi-squared distribution. A specific test is developed on the basis of the difference between the sample estimate and the ensemble average characteristic functions. Preliminary results demonstrate the discriminative power of the test against various types of alternatives
Keywords :
convergence; random processes; signal processing; statistical analysis; time series; Gaussian distribution; chi-squared distribution; discriminative power; ensemble average characteristic functions; multivariate stationary time-series; sample statistics; tests; Artificial intelligence; Gaussian noise; Minimization methods; Roentgenium; Telecommunications; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-0508-6
Type :
conf
DOI :
10.1109/SSAP.1992.246818
Filename :
246818
Link To Document :
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