© 2011

Recursive Estimation and Time-Series Analysis

An Introduction for the Student and Practitioner


Table of contents

  1. Front Matter
    Pages i-xvii
  2. Peter C. Young
    Pages 1-9
  3. Recursive Estimation of Parameters in Linear Regression Models

    1. Front Matter
      Pages 11-11
    2. Peter C. Young
      Pages 29-46
    3. Peter C. Young
      Pages 99-136
  4. Recursive Estimation of Parameters in Transfer Function Models

  5. Other Topics

    1. Front Matter
      Pages 325-325
    2. Peter C. Young
      Pages 327-355
    3. Peter C. Young
      Pages 357-381
  6. Back Matter
    Pages 383-504

About this book


This is a revised version of  the 1984 book of the same name but considerably modified and enlarged to accommodate the developments in recursive estimation and time series analysis that have occurred over the last quarter century. Also over this time, the CAPTAIN Toolbox for recursive estimation and time series analysis has been developed by my colleagues and I at Lancaster, for use in the MatlabTM software environment (see Appendix G). Consequently, the present version of the book is able to exploit the many computational routines that are contained in this widely available Toolbox, as well as some of the other routines in MatlabTM and its other toolboxes.

The book is an introductory one on the topic of recursive estimation and it demonstrates how this approach to estimation, in its various forms, can be an impressive aid to the modelling of stochastic, dynamic systems. It is intended for undergraduate or Masters students who wish to obtain a grounding in this subject; or for practitioners in industry who may have heard of topics dealt with in this book and, while they want to know more about them, may have been deterred by the rather esoteric nature of some books in this challenging area of study.

Authors and affiliations

  1. 1.Haverbreaks, LancasterUnited Kingdom

Bibliographic information

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From the book reviews:

“This book is designed as an introductory reference and is written in an elegant and intuitive manner so as to enable students to understand such important and challenging topics as time series, system identification and recursive estimation methods. … The book is highly recommended for the bookshelf of any student or practitioner who is beginning to deal with stochastic modelling, as well as for academics who need to explore methods beyond standard linear regressions for the process under study.” (Juan R. Trapero, International Journal of Forecasting, October, 2014)