Restricted kalman filtering : theory, methods, and application
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Bibliographic Information
Restricted kalman filtering : theory, methods, and application
（Springer Briefs in statistics）
Springer, c2012
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Includes bibliographical references
Description and Table of Contents
Description
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).
Table of Contents
Introduction.- Linear state space models and the Kalman filtering: a briefing.- Restricted Kalman filtering: theoretical issues.- Restricted Kalman filtering: methodological issues.- Applications.- Further Extensions.
by "Nielsen BookData"