Nonlinear least-squares estimation

David Pollard and Peter Radchenko

Journal of Multivariate Analysis, 2006, vol. 97, issue 2, 548-562

Abstract: The paper uses empirical process techniques to study the asymptotics of the least-squares estimator (LSE) for the fitting of a nonlinear regression function. By combining and extending ideas of Wu and Van de Geer, it establishes new consistency and central limit theorems that hold under only second moment assumptions on the errors. An application to a delicate example of Wu's illustrates the use of the new theorems, leading to a normal approximation to the LSE with unusual logarithmic rescalings.

Keywords: Nonlinear; least; squares; Empirical; processes; Subgaussian; Consistency; Central; limit; theorem (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (7)

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