By George A. F. Seber, C. J. Wild
WILEY-INTERSCIENCE PAPERBACK SERIESThe Wiley-Interscience Paperback sequence contains chosen books which were made extra obtainable to shoppers as a way to elevate worldwide allure and basic move. With those new unabridged softcover volumes, Wiley hopes to increase the lives of those works by way of making them to be had to destiny generations of statisticians, mathematicians, and scientists.From the experiences of Nonlinear Regression"A excellent booklet and a tremendous one in that it's prone to turn into a typical reference for all attracted to nonlinear regression; and that i could think that any statistician excited by nonlinear regression would need a duplicate on his shelves."–The Statistician"Nonlinear Regression additionally features a reference record of over seven hundred entries. The compilation of this fabric and cross-referencing of it really is some of the most worthy points of the booklet. Nonlinear Regression gives you the researcher surprising with a selected area of expertise sector of nonlinear regression an advent to that sector of nonlinear regression and entry to the best references . . . Nonlinear Regression presents through a ways the broadest dialogue of nonlinear regression versions at present to be had and should be a useful addition to the library of a person attracted to realizing and utilizing such versions together with the statistical researcher."–Mathematical experiences
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Additional info for Nonlinear Regression (Wiley Series in Probability and Statistics)
Example text
The problem of nonconstant error variance can be approached by weighting. With linear models, having an error distribution that is closely approximated by the normal is important because the normal assumption allows us to obtain exact inferences in small samples. With nonlinear models, however, we rely almost entirely on obtaining approximate inferences by applying asymptotic results to finite samples. In this respect the asymptotic theory for, say, least squares does not require normally distributed errors.
67). i= y(6) and (c) y(0) has continuous first-order derivatives. 1 and assume that expressions are valid for 6 in N . 64). Thus (i) is proved.
56) 20 Model Building If i refers to time, the above model can be generalized further by allowing x i to include lagged values yi- yi- 2, etc. The above models are not considered further in this book, and the interested reader is referred to Amemiya El9831 and Maddala [1977] for further details. The asymptotic theory for the above models is developed in detail by Gallant [1987: Chapters 6,7]. 1 Nonlinear Least Squares Suppose that we have n observations ( x i , y j ) , i = 1,2,. ,n, from a fixed-regressor nonlinear model with a known functional relationship f.