Statistical Methods in Spectral Estimation

نویسنده

  • Rainer Dahlhaus
چکیده

Suppose a certain variable Xt is measured at discrete equally spaced time points t = 1, ... , T and we want to make assertions on the energy distribution of the frequencies in a harmonic analysis. This energy distribution may for example be used to code the data if Xt is a speech signal or to make a prediction if the Xt are economic data. Instead of using a deterministic approach applied scientists usually use a stochastic approach to model the data and to estimate the energy distribution (e.g. in electrical engineering, geophysics, economics or neurophysiology). One reason is that in a stochastic setup certain fluctuations of the Fourier-transform of the data can be interpreted more naturally (cp. section 2). In this paper we discuss the estimation of the energy distribution in a stochastic model and, more generally, the use of the energy distribution for other purposes, such as the detection of hidden frequencies, the estimation of parameters in parametric models and the prediction of the series. The stochastic framework of this paper is the theory of stationary processes. A stationary process is a stochastic process whose finite dimensional distributions are time invariant (c.f. Brillinger, 1981, section 2.4). In particular the mean and the correlation of neighbouring values of a stationary process remain constant over time. An important feature of stationary processes is the following spectral representation (cf. Brockwell and Davies, 1991, Theorem 4.8.2).

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تاریخ انتشار 1992