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Consistency for the least squares estimator in nonlinear regression model

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  • Shuhe, Hu

Abstract

The consistency problems of the least-squares estimator [theta]n for parameter [theta] in nonlinear regression model are resolved perfectly. Assuming that the tth absolute moments of the model errors are finite, for t[greater-or-equal, slanted]2 and the errors satisfy general dependent conditions, we obtain the same probability inequality as that in Ivanov (Theory Probab. Appl. 21 (1976) 557) which has independent identically distributed errors; for 1

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  • Shuhe, Hu, 2004. "Consistency for the least squares estimator in nonlinear regression model," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 183-192, April.
    • Handle: RePEc:eee:stapro:v:67:y:2004:i:2:p:183-192
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    References listed on IDEAS

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    1. Prakasa Rao, B. L. S., 1984. "The rate of convergence of the least squares estimator in a non-linear regression model with dependent errors," Journal of Multivariate Analysis, Elsevier, vol. 14(3), pages 315-322, June.
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    Cited by:

    1. A. Alessandri & L. Cassettari & R. Mosca, 2009. "Nonparametric nonlinear regression using polynomial and neural approximators: a numerical comparison," Computational Management Science, Springer, vol. 6(1), pages 5-24, February.
    2. Yang, Wenzhi & Hu, Shuhe, 2014. "Large deviation for a least squares estimator in a nonlinear regression model," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 135-144.
    3. Lyubchich, Vyacheslav & Gel, Yulia R., 2016. "A local factor nonparametric test for trend synchronism in multiple time series," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 91-104.

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