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On the recursive parameter estimation in the general discrete time statistical model

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  • Sharia, Teo

Abstract

The consistency and asymptotic linearity of recursive maximum likelihood estimator is proved under some regularity and ergodicity assumptions on the logarithmic derivative of a transition density for a general statistical model. © 1998 Elsevier Science B.V.

Suggested Citation

  • Sharia, Teo, 1998. "On the recursive parameter estimation in the general discrete time statistical model," Stochastic Processes and their Applications, Elsevier, vol. 73(2), pages 151-172, March.
    • Handle: RePEc:eee:spapps:v:73:y:1998:i:2:p:151-172
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    References listed on IDEAS

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    1. Englund, Jan-Eric & Holst, Ulla & Ruppert, David, 1989. "Recursive estimators for stationary, strong mixing processes--a representation theorem and asymptotic distributions," Stochastic Processes and their Applications, Elsevier, vol. 31(2), pages 203-222, April.
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    Cited by:

    1. Teo Sharia, 2014. "Truncated stochastic approximation with moving bounds: convergence," Statistical Inference for Stochastic Processes, Springer, vol. 17(2), pages 163-179, July.
    2. Abdelhamid Ouakasse & Guy Mélard, 2017. "A New Recursive Estimation Method for Single Input Single Output Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 417-457, May.
    3. Teo Sharia, 2008. "Recursive parameter estimation: convergence," Statistical Inference for Stochastic Processes, Springer, vol. 11(2), pages 157-175, June.

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    1. Teo Sharia, 2008. "Recursive parameter estimation: convergence," Statistical Inference for Stochastic Processes, Springer, vol. 11(2), pages 157-175, June.
    2. Teo Sharia, 2014. "Truncated stochastic approximation with moving bounds: convergence," Statistical Inference for Stochastic Processes, Springer, vol. 17(2), pages 163-179, July.

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