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Titlebook: Recursive Estimation and Time-Series Analysis; An Introduction Peter Young Textbook 19841st edition Springer-Verlag, Berlin, Heidelberg 198

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书目名称Recursive Estimation and Time-Series Analysis
副标题An Introduction
编辑Peter Young
视频video
丛书名称Communications and Control Engineering
图书封面Titlebook: Recursive Estimation and Time-Series Analysis; An Introduction Peter Young Textbook 19841st edition Springer-Verlag, Berlin, Heidelberg 198
描述This book has grown out of a set of lecture notes prepared originally for a NATO Summer School on "The Theory and Practice of Systems ModelLing and Identification" held between the 17th and 28th July, 1972 at the Ecole Nationale Superieure de L‘Aeronautique et de L‘Espace. Since this time I have given similar lecture courses in the Control Division of the Engineering Department, University of Cambridge; Department of Mechanical Engineering, University of Western Australia; the University of Ghent, Belgium (during the time I held the IBM Visiting Chair in Simulation for the month of January, 1980), the Australian National University, and the Agricultural University, Wageningen, the Netherlands. As a result, I am grateful to all the reci­ pients of these lecture courses for their help in refining the book to its present form; it is still far from perfect but I hope that it will help the student to become acquainted with the interesting and practically useful concept of recursive estimation. Furthermore, I hope it will stimulate the reader to further study the theoretical aspects of the subject, which are not dealt with in detail in the present text. The book is primarily intended to
出版日期Textbook 19841st edition
关键词Analysis; Matlab; Normal; STATISTICA; algorithms; control; data analysis; filters; information; linear regres
版次1
doihttps://doi.org/10.1007/978-3-642-82336-7
isbn_ebook978-3-642-82336-7Series ISSN 0178-5354 Series E-ISSN 2197-7119
issn_series 0178-5354
copyrightSpringer-Verlag, Berlin, Heidelberg 1984
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Introductionpublished account of the theory in 1805 and, indeed, first coined the term “least squares”, it was Gauss who developed the method into a statistical tool, embedding it within a statistical framework involving a probabilistic treatment of observational errors.
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Recursive Least Squares Regression Analysisameter regression model. But, as we have pointed out, the algorithm is a deterministic estimation procedure in the sense that it makes few assumptions about either the statistical nature of the signals or the noise and does not provide any statistical information on the nature of the estimates.
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The Instrumental Variable (IV) Method of Time-Series Analysisariables. In the recursive IV algorithm (IV) it is required that we generate a vector of instrumental variables . at each recursive step. If this is possible, then it is easy to see why the IV modification is effective in removing the asymptotic bias from the estimates.
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Alternative Recursive Approaches to Time-Series Analysisings in deterministic, linear least squares methods; through the more complicated but still straightforward procedures of statistical regression analysis; to the quite complex techniques for solving the nonlinear problems of time-series analysis. And in this latter time-series case, we have been car
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