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Estimation and test of linearity for a class of additive nonlinear models

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  • Chèze-Payaud, Nathalie
  • Poggi, Jean-Michel
  • Portier, Bruno

Abstract

This paper deals with the estimation and the test for linearity of models belonging to a class of additive nonlinear ones. We prove the joint asymptotic normality for a kernel estimator and provide a test for linearity of each function defining the model.

Suggested Citation

  • Chèze-Payaud, Nathalie & Poggi, Jean-Michel & Portier, Bruno, 1998. "Estimation and test of linearity for a class of additive nonlinear models," Statistics & Probability Letters, Elsevier, vol. 40(2), pages 189-201, September.
    • Handle: RePEc:eee:stapro:v:40:y:1998:i:2:p:189-201
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    References listed on IDEAS

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    1. Chen, R. & Härdle, Wolfgang & Linton, O. B. & Severance-Lossin, E., 1995. "Nonparametric Estimation of Additive Seperable Regression Models," SFB 373 Discussion Papers 1995,50, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Jean‐Michel Poggi & Bruno Portier, 1997. "A Test of Linearity for Functional Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(6), pages 615-639, November.
    3. W. Härdle & P. Hall, 1993. "On the backfitting algorithm for additive regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 47(1), pages 43-57, March.
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