Nonlinear Varying-Coefficient Models with Applications to a Photosynthesis Study

Esra Kürüm, Runze Li, Yang Wang, Damla Şentürk

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)57-81
Number of pages25
JournalJournal of Agricultural, Biological, and Environmental Statistics
Volume19
Issue number1
DOIs
StatePublished - Mar 1 2014

Fingerprint

Varying Coefficient Model
Photosynthesis
Ecology
Local Linear Estimator
Ecosystem
Nonlinear Model
photosynthesis
Confidence Intervals
confidence interval
Bootstrap Confidence Intervals
Confidence Bands
Varying Coefficients
F Test
Coefficient
ecology
Asymptotic Normality
Ecosystems
methodology
Covariates
ecosystems

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Agricultural and Biological Sciences (miscellaneous)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

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Nonlinear Varying-Coefficient Models with Applications to a Photosynthesis Study. / Kürüm, Esra; Li, Runze; Wang, Yang; Şentürk, Damla.

In: Journal of Agricultural, Biological, and Environmental Statistics, Vol. 19, No. 1, 01.03.2014, p. 57-81.

Research output: Contribution to journalArticle

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