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Activity Number: 68
Type: Contributed
Date/Time: Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #303695
Title: Saddlepoint-Based Bootstrap Inference About Smoothing Parameters in Nonparametric and Semiparametric Spline Models
Author(s): Robert Paige*+ and Alex Trindade
Companies: Missouri University of Science and Technology and Texas Tech University
Address: , , ,
Keywords: spline regression ; partially linear models ; parametric bootstrap confidence intervals ; saddlepoint approximation ; estimating equation
Abstract:

We propose a novel method for making small sample inference on smoothing parameters in spline regression models. A parametric bootstrap method is developed where Monte Carlo simulation is replaced by saddlepoint approximation. Saddlepoint approximations to the distribution of the estimating equation whose unique root is the parameter's estimator are obtained, while substituting penalized maximum likelihood estimates for the remaining (nuisance) parameters. A key result of Daniels (1983) enables us to relate these approximations to those for the estimator of interest. Comparisons are made with existing methods.

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