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Nonparametric estimation for quadratic regression

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  • Chatterjee, Samprit
  • Olkin, Ingram

Abstract

The method of least squares provides the most widely used algorithm for fitting a linear model. A variety of nonparametric procedures have been developed that are designed to be robust against model violations and resistant against aberrant points. One such method introduced by Theil [1950. A rank-invariant method of linear and polynomial regression analysis. I, II, III. Proc. Ned. Akad. Wet. 53, 386-392, 521-525, 1397-1412] is based on pairwise estimates. There are many examples in which the data are nonlinear, and in particular, where a quadratic fit may be more appropriate. We here propose a nonparametric method for fitting a quadratic regression.

Suggested Citation

  • Chatterjee, Samprit & Olkin, Ingram, 2006. "Nonparametric estimation for quadratic regression," Statistics & Probability Letters, Elsevier, vol. 76(11), pages 1156-1163, June.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:11:p:1156-1163
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    Cited by:

    1. Claudio G. Borroni & D. Michele Cifarelli, 2016. "Some maximum-indifference estimators for the slope of a univariate linear model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 395-412, June.

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