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＜電子ブック＞
Restricted Kalman Filtering : Theory, Methods, and Application / by Adrian Pizzinga
(SpringerBriefs in Statistics. ISSN:21915458 ; 12)

版	1st ed. 2012.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2012
大きさ	X, 62 p. 9 illus : online resource
著者標目	*Pizzinga, Adrian author SpringerLink (Online service)
件　名	LCSH:Statistics  FREE:Statistical Theory and Methods FREE:Statistics FREE:Statistics in Business, Management, Economics, Finance, Insurance
一般注記	Introduction -- Linear state space models and the Kalman filtering: a briefing -- Restricted Kalman filtering: theoretical issues -- Restricted Kalman filtering: methodological issues -- Applications -- Further Extensions 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) HTTP:URL=https://doi.org/10.1007/978-1-4614-4738-2

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