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Maximum likelihood estimator in a two-phase nonlinear random regression model

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  • Ciuperca Gabriela

Abstract

We consider a two-phase random design nonlinear regression model, the regression function is discontinuous at the change-point. The errors ∊ are arbitrary, with E(∊) = 0 and E(∊2)

Suggested Citation

  • Ciuperca Gabriela, 2004. "Maximum likelihood estimator in a two-phase nonlinear random regression model," Statistics & Risk Modeling, De Gruyter, vol. 22(4), pages 335-349, April.
    • Handle: RePEc:bpj:strimo:v:22:y:2004:i:4/2004:p:335-349:n:6
    DOI: 10.1524/stnd.22.4.335.64312
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    References listed on IDEAS

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    1. Grégoire, Gérard & Hamrouni, Zouhir, 2002. "Change Point Estimation by Local Linear Smoothing," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 56-83, October.
    2. Facer, Matthew R. & Müller, Hans-Georg, 2003. "Nonparametric estimation of the location of a maximum in a response surface," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 191-217, October.
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    Cited by:

    1. Ciuperca, Gabriela, 2009. "The M-estimation in a multi-phase random nonlinear model," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 573-580, March.
    2. Sergueï Dachian & Ilia Negri, 2011. "On compound Poisson processes arising in change-point type statistical models as limiting likelihood ratios," Statistical Inference for Stochastic Processes, Springer, vol. 14(3), pages 255-271, October.
    3. Fujii, Takayuki, 2008. "On weak convergence of the likelihood ratio process in multi-phase regression models," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 2066-2074, October.

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