Kernel regression estimation when the boundary region is large /

Kernel regression estimation when the boundary region is large /

It is well known that kernel regression estimators are subject to so-called boundary or edge effects, a phenomenon in which the bias of an estimator increases near the endpoints of the estimation interval. Boundary kernels have been proposed by Gasser and Müller (1979) and Rice (1984) to deal with e...

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Bibliographic Details
Main Authors: Hart, Jeffrey D. (Author), Wehrly, Thomas E., 1947- (Author)
Corporate Author: United States. Office of Naval Research (sponsoring body.)
Format: Book
Language:English
Published: College Station, Texas : Department of Statistics, Texas A & M University, [1990]
Series:Technical report (Texas A & M University. Department of Statistics) ; no. 91.
Subjects:
Kernel functions.
Regression analysis.
Nonparametric statistics.
Estimation theory.
Smoothing (Statistics)
Least squares.
Goodness-of-fit tests.
Mathematical statistics.
Statistics > Nonparametric inference > Nonparametric regression.
Smoothing parameter selection
Cross-validation

Cushing: Texas A&M Publications (Remote Storage: 2-3 day retrieval)

Holdings details from Cushing: Texas A&M Publications (Remote Storage: 2-3 day retrieval)
Call Number: QA276.A12 T4 no.91
 
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QA276.A12 T4 no.91 Available