Restricted Kalman filtering : theory, methods, and application

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 measu...

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1. Verfasser: Pizzinga, Adrian, 19..-
Format: Livre numérique
Sprache:Anglais
Veröffentlicht: New York, NY : Springer New York [20..].
Cham : Springer Nature
Ausgabe:1st ed. 2012.
Schriftenreihe:SpringerBriefs in Statistics 12
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Anmerkung: Archives Springer e-books (Licence nationale)
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Edition sous un autre format:• Restricted Kalman Filtering, Texte imprimé, 9781461447375
• Restricted Kalman Filtering, Texte imprimé, 9781461447375
• Restricted Kalman Filtering, Texte imprimé, 9781461447399
• Restricted Kalman Filtering, Texte imprimé, 9781461447375
• Restricted Kalman Filtering, Texte imprimé, 9781461447399
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505 1 |a Introduction Linear state space models and the Kalman filtering: a briefing Restricted Kalman filtering: theoretical issues Restricted Kalman filtering: methodological issues Applications Further Extensions 
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520 |a 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) 
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