By Dr. T. Subba Rao, Dr. M. M. Gabr (auth.)

The conception of time sequence versions has been good built over the past thirt,y years. either the frequenc.y area and time area ways were standard within the research of linear time sequence versions. even though. many actual phenomena can't be accurately represented by means of linear versions; consequently the need of nonlinear types and better order spectra. lately a couple of nonlinear versions were proposed. during this monograph we limit recognition to 1 specific nonlinear version. referred to as the "bilinear model". the main fascinating function of this type of version is that its moment order covariance research is ve~ just like that for a linear version. This demonstrates the significance of upper order covariance research for nonlinear versions. For bilinear types it's also attainable to procure analytic expressions for covariances. spectra. and so on. that are frequently tough to acquire for different proposed nonlinear types. Estimation of bispectrum and its use within the development of checks for linearit,y and symmetry also are mentioned. the entire tools are illustrated with simulated and actual info. the 1st writer wish to recognize the ease he acquired within the practise of this monograph from providing a chain of lectures relating to bilinear versions on the collage of Bielefeld. Ecole Normale Superieure. collage of Paris (South) and the Mathematisch Cen trum. Ams terdam.

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Extra resources for An Introduction to Bispectral Analysis and Bilinear Time Series Models

Example text

We now obtain an expression for the bias of the bispectral estimate. the derivation of which is essentially similar to the one given by Brillinger and Rosenblatt (1967a) for the k-th order case. 8). Ftftther let Ko(8 1 ,82 ) ecK(2). 12) The mean square error is a function of V2 and BK only when the second order spectral density functions f(Wl), f(W2) and f(Wl+W2) are given. We assume that the spectral windows which are non-negative and belong to c/ 2 ) class give rise effectively to the same bias when used for estimating the bispectral density function.

Few) is a consistent estimate of f(III). The basic problem in the estimation of f(lII) is to find a suitable weight function KM(e). During the decade 1955-1965 several authors, notably Lominicki and Zaremba (1957). Parzen (1957. 1958). Whittle (1957). Tukey (1959). Bartlett (1948. 1950. 1966), and Priestley (1962) have suggested various windows. some of which depend on unknown parameters of the spectral density function f(III). A comparison of these windows has been made by Neave (1972). 1. 1 have characteristic exponent 2.

M+ CD but MIN + O. (If A(s) = 0, lsI> 1, M corresponds to the "truncation point"). The function A(s) is known as a lag window. Parzen defi ned the "characteri s ti c exponent" of the functi on Ko (e) as the largest integer r, such that exists, is finite and non-zero. Parzen (1957) has shown that the most useful class of windows to consider is that for which r = 2. We have the well known asymptotic results (see, e. g. Priestley, 1981), 32 A E[f(lII)] '" f(lII) var[f(III)J '" j! 6) from which it follows that.

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