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Local polynomial fitting under association

โœ Scribed by Elias Masry


Book ID
104269865
Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
297 KB
Volume
86
Category
Article
ISSN
0047-259X

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โœฆ Synopsis


We consider the estimation of multivariate regression functions rรฐx 1 ; y; x d รž and their partial derivatives up to a total order pX1 using high-order local polynomial fitting. The processes fY i ; X i g are assumed to be (jointly) associated. Joint asymptotic normality is established for the estimates of the regression function r and all its partial derivatives up to the total order p: Expressions for the bias and variance/covariance matrix (of the asymptotic distribution) are given.


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Varying coefficient models are useful extensions of the classical linear models. Under the condition that the coefficient functions possess about the same degrees of smoothness, the model can easily be estimated via simple local regression. This leads to the one-step estimation procedure. In this pa