JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 98
Type: Invited
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Journal of Nonparmametric Statistics
Abstract - #303697
Title: Nonlinear Nonparametric Regression Models
Author(s): Yuedong Wang*+ and Chunlei Ke
Companies: University of California at Santa Barbara and Amgen, Inc.
Address: Department of Statisitcs and Applied Probability, santa barbara, CA, 93106,
Keywords: smoothing spline ; penalized likelihood ; nonlinear functional ; extended Gauss-Newton algorithm ; nonlinear Gauss-Seidel algorithm ; nonlinear mixed effects model
Abstract:

Almost all of the current nonparametric regression methods such as smoothing splines, generalized additive models and varying coefficients models assume a linear relationship when nonparametric functions are regarded as parameters. In this talk we present a general class of smoothing spline nonlinear nonparametric models that allow nonparametric functions to act nonlinearly. They arise in many fields as either theoretical or empirical models. Our new estimation methods are based on an extension of the Gauss-Newton method to infinite dimensional spaces and the backfitting procedure. We extend the generalized cross validation and the generalized maximum likelihood methods to estimate smoothing parameters. We establish connections between some nonlinear nonparametric models and nonlinear mixed effects models. Approximate Bayesian confidence intervals are derived for inference. We illustrate the methods with an application to term structure of interest rates. Simulations are conducted to evaluate finite-sample performance of our methods.


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program




2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.