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Asymptotic theory of least squares estimator of a particular nonlinear regression model

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  • Kundu, Debasis

Abstract

The consistency and asymptotic normality of the least squares estimator are derived for a particular non-linear regression model, which does not satisfy the standard sufficient conditions of Jennrich (1969) or Wu (1981), under the assumption of normal errors.

Suggested Citation

  • Kundu, Debasis, 1993. "Asymptotic theory of least squares estimator of a particular nonlinear regression model," Statistics & Probability Letters, Elsevier, vol. 18(1), pages 13-17, August.
    • Handle: RePEc:eee:stapro:v:18:y:1993:i:1:p:13-17
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    Citations

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    Cited by:

    1. Bansal, Naveen K. & Hamedani, G. G. & Zhang, Hao, 1999. "Non-linear regression with multidimensional indices," Statistics & Probability Letters, Elsevier, vol. 45(2), pages 175-186, November.
    2. Kim, Tae Soo & Kim, Hae Kyung & Hur, Sun, 2002. "Asymptotic properties of a particular nonlinear regression quantile estimation," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 387-394, December.
    3. Katkovnik, Vladimir, 1997. "Nonparametric estimation of the time-varying frequency and amplitude," Statistics & Probability Letters, Elsevier, vol. 35(4), pages 307-315, November.
    4. Pramesti Getut, 2023. "Parameter least-squares estimation for time-inhomogeneous Ornstein–Uhlenbeck process," Monte Carlo Methods and Applications, De Gruyter, vol. 29(1), pages 1-32, March.
    5. Juan R. A. Bobenrieth & Eugenio S. A. Bobenrieth & Andrés F. Villegas & Brian D. Wright, 2022. "Estimation of Endogenous Volatility Models with Exponential Trends," Mathematics, MDPI, vol. 10(15), pages 1-27, July.

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