Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory
by Steven Kay
This volume introduces parameter estimation for time series data through principal approaches, estimator selection, complex-valued data and parameters, practical examples, and computer-based performance assessment. Its emphasis on linear models supports finding optimal estimators directly from a data model, making it a focused reference for statistical signal processing.
About This Book
Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory presents the field of parameter estimation as applied to time series data.
The book summarizes principal estimation approaches and provides a roadmap for selecting an estimator suited to a given data model.
It extends results from real data and real parameters to complex data and complex parameters, while surveying important estimators used in practice.
Examples show how a digital computer can be used to assess estimator performance.
The presentation emphasizes linear models, making it possible to identify an optimal estimator directly from the data model.
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