| 书目名称 | Nonlinear Filters | | 副标题 | Estimation and Appli | | 编辑 | Hisashi Tanizaki | | 视频video | | | 丛书名称 | Lecture Notes in Economics and Mathematical Systems | | 图书封面 |  | | 描述 | For a nonlinear filtering problem, the most heuristic andeasiest approximation is to use the Taylor series expansionand apply the conventional linear recursive Kalman filteralgorithm directly to the linearized nonlinear measurementand transition equations. First, it is discussed that theTaylor series expansion approach gives us thebiasedestimators. Next, a Monte-Carlo simulation filter isproposed, where each expectation of the nonlinear functionsis evaluated generating random draws. It is shown fromMonte-Carlo experiments that the Monte-Carlo simulationfilter yields the unbiased but inefficient estimator.Anotherapproach to the nonlinear filtering problem is toapproximate the underlyingdensity functions of the statevector. In this monograph, a nonlinear and nonnormal filteris proposed by utilizing Monte-Carlo integration, in which arecursive algorithm of the weighting functions is derived.The densityapproximation approach gives us anasymptotically unbiased estimator. Moreover, in terms ofprogramming and computational time, the nonlinear filterusing Monte-Carlo integration can be easily extended tohigher dimensional cases, compared with Kitagawa‘s nonlinearfilter using numericalinteg | | 出版日期 | Book 19931st edition | | 关键词 | Kalman Filter; Nichtlineare Filter; Nonlineare Filter; Normal; Transit; econometrics; estimator; filtering; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-662-22237-9 | | isbn_ebook | 978-3-662-22237-9Series ISSN 0075-8442 Series E-ISSN 2196-9957 | | issn_series | 0075-8442 | | copyright | Springer-Verlag Berlin Heidelberg 1993 |
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发表于 2025-3-21 18:04:20
