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Activity Number: 153
Type: Contributed
Date/Time: Monday, August 7, 2006 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #307420
Title: Shape-Restricted Regression Splines and Applications
Author(s): Mary Meyer*+
Companies: University of Georgia
Address: Statistics Building, Athens, GA, 30605,
Keywords: smoothing ; nonparametric ; splines ; regression
Abstract:

Regression splines are smooth and parsimonious nonparametric regression estimators, but are known to be sensitive to the number and placement of the knots. When shape restrictions such as monotonicity or convexity can be imposed, the fits are robust to knot choices. Consequently, inference about the regression function is feasible. Several examples of practical applications are given.

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