Nonparametric estimation for quadratic regression

Samprit Chatterjee and Ingram Olkin

Statistics & Probability Letters, 2006, vol. 76, issue 11, 1156-1163

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.

Keywords: Distribution-free; regression; Theil; estimator; Least; squares; regression; Robust; regression (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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