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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 generalized F test 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.

SUBMITTER: Kurum E

PROVIDER: S-EPMC4070621 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Nonlinear Varying Coefficient Models with Applications to Studying Photosynthesis.

Kürüm Esra E Li Runze R Wang Yang Y SEntürk Damla D

Journal of agricultural, biological, and environmental statistics 20140301 1

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 esti ...[more]

PMID: 24976756

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