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Stochastic Approximation Nonlinear Regression by Albert Arthur (11 results)
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Stochastic Approximation and Nonlinear Regression (Research monograph #42)
Published by M.I.T. Press
Hardcover. Condition: Fair. No Jacket. Former library book; Missing dust jacket; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less 1.
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Condition: New. Stochastic Approximation and NonLinear Regression (Paperback or Softback) 0.7.
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US$ 28.76
Convert currencyUS$ 20.22 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketSoft cover. Condition: Fine.
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Condition: New. pp. 220.
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Paperback. Condition: New. In shrink wrap. Looks like an interesting title!
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Stochastic Approximation and NonLinear Regression
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
US$ 49.60
Convert currencyUS$ 5.63 shipping from United Kingdom to U.S.A.Quantity: 15 available
Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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US$ 45.04
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Add to basketCondition: New. Print on Demand pp. 220.
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US$ 50.83
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Add to basketCondition: New. PRINT ON DEMAND pp. 220.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Convert currencyUS$ 72.84 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This monograph addresses the problem of real-time curve fitting in the presence of noise, from the computational and statistical viewpoints. It examines the problem of nonlinear regression, where observations are made on a time series whose mean-value function is known except for a vector parameter. In contrast to the traditional formulation, data are imagined to arrive in temporal succession. The estimation is carried out in real time so that, at each instant, the parameter estimate fully reflects all available data. Specifically, the monograph focuses on estimator sequences of the so-called differential correction type. The term differential correction refers to the fact that the difference between the components of the updated and previous estimators is proportional to the difference between the current observation and the value that would be predicted by the regression function if the previous estimate were in fact the true value of the unknown vector parameter. The vector of proportionality factors (which is generally time varying and can depend upon previous estimates) is called the gain or smoothing vector. The main purpose of this research is to relate the large-sample statistical behavior of such estimates (consistency, rate of convergence, large-sample distribution theory, asymptotic efficiency) to the properties of the regression function and the choice of smoothing vectors. Furthermore, consideration is given to the tradeoff that can be effected between computational simplicity and statistical efficiency through the choice of gains.
