Restricted Kalman Filtering : Theory, Methods, and Application

Adrian Pizzinga

ISBN 10: 1461447372 / ISBN 13: 9781461447375

Edité par Springer New York, Springer, 2012

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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).

N° de réf. du vendeur 9781461447375

Détails bibliographiques

• Titre: Restricted Kalman Filtering : Theory, Methods, and Application
• Éditeur: Springer New York, Springer
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 1461447372
• ISBN à 13 chiffres: 9781461447375
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 5 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 5 millimètres de profondeur
• Poids: 125 grammes

A propos de ce titre

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).

À propos de l?auteur

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