Abstract
Abstract : In Part 1, the general procedures of the conventional method of correlation or spectrum analysis of a random process (nonparametric method) are reviewed, stressing the statistical reliability of the results. A few suggestions for improving coherencies are given. In Part II, the characteristics of AR, MA, and ARMA models are discussed. The model-fitting technique supported by AIC criteria is introduced, with the examples of application to seakeeping data. In Part M, the statistical treatments of nonlinearities in random process analysis are summarized and reviewed. Conclusions are given, and future work is proposed. A review of statistical studies, Seakeeping qualities, Stochastic processes, Parametric analysis of time series, Nonparametric analysis of time series, Spectrum analysis, Nonlinear stochastic process analysis, Model fitting techniques to a time series, AR Model, MA Model, ARMA Model and AIC criterion.
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