We propose an extension to time series with several simultaneously measured
variables of the nonlinearity test, which combines the redundancy -- linear
redundancy approach with the surrogate data technique. For several variables
various types of the redundancies can be defined, in order to test specific
dependence structures between/among (groups of) variables. The null hypothesis
of a multivariate linear stochastic process is tested using the multivariate
surrogate data. The linear redundancies are used in order to avoid spurious
results due to imperfect surrogates. The method is demonstrated using two types
of numerically generated multivariate series (linear and nonlinear) and
experimental multivariate data from meteorology and physiology.Comment: 11 pages, compressed and uuencoded postscript file, figures included.
Also available by anonymous ftp at ftp://ftp.santafe.edu/pub/mp/multi,
E-mail: [email protected], [email protected]
