Science/Engineering Piet M.T. Broersen - Automatic Autocorrelation and Spectral Analysis (Repost) (1846283280)

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Piet M.T. Broersen - Automatic Autocorrelation and Spectral Analysis

& 8220;The subject of this book is the description of the main properties of univariate

stationary stochastic signals. A univariate signal is a single observed variable that

varies as a function of time or position. Stochastic (or random) loosely means that

the measured signal looks different every time an experiment is repeated. However,

the process that generates the signal is still the same. Stationary indicates that the

statistical properties of the signal are constant in time. The properties of a

stochastic signal are fully described by the joint probability density function of the

observations. This density would give all information about the signal, if it could

be estimated from the observations. Unfortunately, that is generally not possible

without very much additional knowledge about the process that generated the

observations. General characteristics that can always be estimated are the power

spectral density that describes the frequency content of a signal and the autocovariance

function that indicates how fast a signal can change in time. Estimation

of spectrum or autocovariance is the main purpose of time series identification.

This knowledge is sufficient for an exact description of the joint probability density

function of normally distributed observations. For observations with other

densities, it is also useful information.& 8221;

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	Science/Engineering Automatic Autocorrelation and Spectral Analysis 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 ...
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