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Generalised signed-rank estimation for nonlinear models with multidimensional indices

Author

Listed:
  • Brice M. Nguelifack
  • Eddy A. Kwessi
  • Asheber Abebe

Abstract

We consider a nonlinear regression model when the index variable is multidimensional. Such models are useful in signal processing, texture modelling, and spatio-temporal data analysis. A generalised form of the signed-rank estimator of the nonlinear regression coefficients is proposed. This general form of the signed-rank (SR) estimator includes estimators and hybrid variants. Sufficient conditions for strong consistency and asymptotic normality of the estimator are given. It is shown that the rate of convergence to normality can be different from . The sufficient conditions are weak in the sense that they are satisfied by harmonic-type functions for which results in the current literature may not apply. A simulation study shows that certain generalised SR estimators (e.g. signed rank) perform better in recovering signals than others (e.g. least squares) when the error distribution is contaminated or is heavy-tailed.

Suggested Citation

  • Brice M. Nguelifack & Eddy A. Kwessi & Asheber Abebe, 2015. "Generalised signed-rank estimation for nonlinear models with multidimensional indices," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(2), pages 215-228, June.
    • Handle: RePEc:taf:gnstxx:v:27:y:2015:i:2:p:215-228
    DOI: 10.1080/10485252.2015.1029474
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