Automatic Autocorrelation and Spectral Analysis [Paperback]

Spectral analysis requires subjective decisions which influence the final estimate and mean that different analysts can obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that is only acceptable if it is close to the best attainable accuracy for most types of stationary data. This book describes a method which fulfils the above near-optimal-solution criterion, taking advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data.

This book describes a method which fulfils optimal solution criterion, taking advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data.

Automatic Autocorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively.

In the current practice of spectral analysis, subjective decisions have to be made all of which influence the final spectral estimate and mean that different analysts obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that solution is only acceptable if it is close to the best attainable accuracy for most types of stationary data.

Automatic Autocorrelation and Spectral Analysis describes a method which fulfils the near-optimal-solution criterion. It takes advantage of greater computing power and robust algorithms to produce enough models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the beslC