A survey of nonlinear system identification algorithms and related topics is presented by
extracting significant results from the literature and presenting these in an organised and …

Part I deals with the mathematical preliminaries: numerical linear algebra; system theory;
stochastic processes; and Kalman filtering. Part II explains realization theory as applied to …

With a focus on analyzing and modeling linear dynamic systems using statistical methods,
Time Series Analysis formulates various linear models, discusses their theoretical …

Bridging the gap between theory and practice for modern statistical model building,
Introduction to General and Generalized Linear Models presents likelihood-based …

In this book, the author adopts a state space approach to time series modeling to provide a
new, computer-oriented method for building models for vector-valued time series. This …

Advanced undergraduates and graduate students of electrical, chemical, mechanical, and
environmental engineering will appreciate this text for a course in systems identification. In …

This paper provides a methodology for optimally locating sensors in a dynamic system so
that data acquired from those locations will yield the best identification of the parameters to …

In this paper, we present a brief survey of literature concerned with fitting ARMA models to
earthquake record data. This is, in a sense, relatively new for the earthquake record …

During the last two decades, considerable progress has been made in statistical time series
analysis. The aim of this book is to present a survey of one of the most active areas in this …

This is the final paper in a series of three which have been concerned with the
comprehensive evaluation of the refined instrumental variable (IV) method of recursive time …