期刊
JOURNAL OF MULTIVARIATE ANALYSIS
卷 97, 期 2, 页码 548-562出版社
ELSEVIER INC
DOI: 10.1016/j.jmva.2005.04.002
关键词
nonlinear least squares; empirical processes; subgaussian; consistency; central limit theorem
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. (C) 2005 Elsevier Inc. All rights reserved.
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