Restricted Kalman Filtering

Adrian Pizzinga

ISBN 10: 1461447372 / ISBN 13: 9781461447375

Published by Springer New York, Springer Jul 2012, 2012

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -¿¿¿¿¿¿¿¿ ¿In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variationsand other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Briefoffers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where the proposed methods are illustrated and evaluated. The Briefhas a short chapter on linear state space models and the Kalman filter, aiming to make the book self-contained and to give a quick reference to the reader (notation and terminology). The prerequisites would be a contact with time series analysis in the level of Hamilton (1994) or Brockwell & Davis (2002) and also with linear state models and the Kalman filter ¿ each of these books has a chapter entirely dedicated to the subject. The book is intended for graduate students, researchers and practitioners in statistics (specifically: time series analysis and econometrics).Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 72 pp. Englisch.

Seller Inventory # 9781461447375

Bibliographic Details

• Title: Restricted Kalman Filtering
• Publisher: Springer New York, Springer Jul 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 1461447372
• ISBN 13: 9781461447375
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 5D millimeters 235 millimeters width by 155 millimeters height by 5 millimeters depth
• Weight: 125 grams

About this title

Synopsis

​​​​​​​​ ​In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone.  This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where the proposed methods are illustrated and evaluated. The Brief has a short chapter on linear state space models and the Kalman filter, aiming to make the book self-contained and to give a quick reference to the reader (notation and terminology). The prerequisites would be a contact with time series analysis in the level of Hamilton (1994) or Brockwell & Davis (2002) and also with linear state models and the Kalman filter – each of these books has a chapter entirely dedicated to the subject. The book is intended for graduate students, researchers and practitioners in statistics (specifically: time series analysis and econometrics).

About the Author

Adrian Pizzinga, Department of Statistics, Institute of Mathematics and Statistics, Fluminense Federal University (UFF) Rio de Janeiro, Brazil