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Moderate deviation principle in nonlinear bifurcating autoregressive models

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  • Bitseki Penda, S. Valère
  • Olivier, Adélaïde

Abstract

Recently, nonparametric techniques have been proposed to study bifurcating autoregressive processes. One can build Nadaraya–Watson type estimators of the two autoregressive functions as in Bitseki Penda et al. (2017) and Bitseki Penda and Olivier (2017). In the present work, we prove moderate deviation principle for these estimators.

Suggested Citation

  • Bitseki Penda, S. Valère & Olivier, Adélaïde, 2018. "Moderate deviation principle in nonlinear bifurcating autoregressive models," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 20-26.
    • Handle: RePEc:eee:stapro:v:138:y:2018:i:c:p:20-26
    DOI: 10.1016/j.spl.2018.02.037
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    References listed on IDEAS

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    1. Djamal Louani, 1998. "Large Deviations Limit Theorems for the Kernel Density Estimator," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 243-253, March.
    2. S. Valère Bitseki Penda & Adélaïde Olivier, 2017. "Autoregressive functions estimation in nonlinear bifurcating autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 20(2), pages 179-210, July.
    3. Bercu, Bernard & Blandin, Vassili, 2015. "A Rademacher–Menchov approach for random coefficient bifurcating autoregressive processes," Stochastic Processes and their Applications, Elsevier, vol. 125(4), pages 1218-1243.
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    Cited by:

    1. Bitseki Penda, S. Valère, 2023. "Moderate deviation principles for kernel estimator of invariant density in bifurcating Markov chains," Stochastic Processes and their Applications, Elsevier, vol. 158(C), pages 282-314.

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