title: Nonlinear Varying Coefficient Models with Applications to Studying Photosynthesis.
abstract: Motivated by a study on factors affecting the level of photosynthetic activity in a natural ecosystem, we propose nonlinear varying coefficient models, in which the relationship between the predictors and the response variable is allowed to be nonlinear. One-step local linear estimators are developed for the nonlinear varying coefficient models and their asymptotic normality is established leading to point-wise asymptotic confidence bands for the coefficient functions. Two-step local linear estimators are also proposed for cases where the varying coefficient functions admit different degrees of smoothness; bootstrap confidence intervals are utilized for inference based on the two-step estimators. We further propose a generalizedFtest to study whether the coefficient functions vary over a covariate. We illustrate the proposed methodology via an application to an ecology data set and study the finite sample performance by Monte Carlo simulation studies.
authors: Department of Statistics, Istanbul Medeniyet University, Istanbul, Turkey. ( esra.kurum@medeniyet.edu.tr ).
authors: Esra
authors: E
authors: Kürüm
authors: Department of Statistics and The Methodology Center, The Pennsylvania State University, University Park, PA 16802-2111, USA ( rzli@psu.edu ).
authors: Runze
authors: R
authors: Li
authors: Credit & Counterparty Risk Management, McLean, VA 22102, USA ( yangwang@freddiemac.com ).
authors: Yang
authors: Y
authors: Wang
authors: Department of Biostatistics, University of California, Los Angeles, CA 90095, USA ( dsenturk@ucla.edu ).
authors: Damla
authors: D
authors: SEntürk
keywords: Generalized F test; Local linear regression; Nonlinear regression model; Varying coefficient models