consideration of the windows is very important and also a troublesome problem to get 
reliable results. 
Parametric estimation of the spectral function, explained in Part I, is a method of 
fitting a certain statistical model to the process to be analyzed and then estimating the 
parameters of that model. This method is based on time domain characteristics, and the 
model fitted is closely related to the equations of motion of the process itself. The method 
represents a different approach to spectrum analysis and, this author believes, that is a 
promising one, supplementing the conventional nonparametric approach, treated in Part I. 
So, in Part II, several types of discrete parameter models will be introduced in detail, and 
then the results of application of the model fitting method to the analysis of seakeeping 
data will be shown. By this method, the actual response system, in which the output is fed 
back to the input to some extent, can also be tackled. This kind of system has been hard to 
be analyzed by the conventional method. 
1.4 TREATMENT OF NONLINEARITIES 
One of the reasons for low coherencies in linear spectral analysis is the existence of 
nonlinearities in the response or in the input. So in Part II, the treatment of nonlinearities 
in process analysis is reviewed. One of the greatest achievements in this field for the anal- 
ysis of seakeeping data is due to John F. Dalzell!®1” in the application of polyspectra. 
This application will not be described in detail, except the basic idea, of this treatment but 
another review of the treatment of nonlinearities will be given, to make clear the mutual 
relationships of these several different approaches to the problem. In addition, the treat- 
ment of nonlinearity in response characteristics during one trial has been introduced in 
Section 3.4 in Part I, as an example of multi—input single-output analysis. 
1.5 SCOPE OF STUDIES 
In statistical studies on seakeeping, there are roughly two kinds of applications. One 
is based on the invariant characteristics of a ship itself, and its behavior is studied statisti- 
cally, assuming the excitement from the environment is also stationary. The other 
involves the macroscopic probabilistic distributions of seakeeping behavior, assuming a 
variety of changes in environmental conditions. The former is sometimes called short— 
term statistics, and the latter, long—term statistics. They are, of course, closely related, and 
the short-term statistics are usually used as the basis for studying the long-term statistics. 
Here mostly short—term statistics will be treated. 
