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Estimation of cusp in nonregular nonlinear regression models

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  • Prakasa Rao, B. L. S.

Abstract

The asymptotic properties of the least squares estimator of the cusp in some nonlinear nonregular regression models is investigated via the study of the weak convergence of the least squares process generalizing earlier results in Prakasa Rao (Statist. Probab. Lett. 3 (1985) 15).

Suggested Citation

  • Prakasa Rao, B. L. S., 2004. "Estimation of cusp in nonregular nonlinear regression models," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 243-251, February.
    • Handle: RePEc:eee:jmvana:v:88:y:2004:i:2:p:243-251
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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.
    2. Rao B. L. S. Prakasa, 1986. "Weak Convergence Of The Least Squares Random Field In The Smooth Case," Statistics & Risk Modeling, De Gruyter, vol. 4(4), pages 363-378, April.
    3. Rao, B. L. S. Prakasa, 1985. "Asymptotic theory of least squares estimator in a nonregular nonlinear regression model," Statistics & Probability Letters, Elsevier, vol. 3(1), pages 15-18, February.
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    Cited by:

    1. 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.
    2. S. Dachian & N. Kordzakhia & Yu. A. Kutoyants & A. Novikov, 2018. "Estimation of cusp location of stochastic processes: a survey," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 345-362, July.
    3. O. V. Chernoyarov & S. Dachian & Yu. A. Kutoyants, 2018. "On parameter estimation for cusp-type signals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 39-62, February.
    4. Fujii Takayuki, 2009. "Cusp estimation in random design regression models," Statistics & Risk Modeling, De Gruyter, vol. 27(3), pages 235-248, December.

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