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Minimum density power divergence estimator for Poisson autoregressive models

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  • Kang, Jiwon
  • Lee, Sangyeol

Abstract

The robust estimation for Poisson autoregressive models is studied. As a robust estimator, a minimum density power divergence estimator (MDPDE) is considered. It is shown that under regularity conditions, the MDPDE is strongly consistent and asymptotically normal. Simulation results are provided for illustration. A real data analysis is implemented for the polio incidence data.

Suggested Citation

  • Kang, Jiwon & Lee, Sangyeol, 2014. "Minimum density power divergence estimator for Poisson autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 44-56.
    • Handle: RePEc:eee:csdana:v:80:y:2014:i:c:p:44-56
    DOI: 10.1016/j.csda.2014.06.009
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    References listed on IDEAS

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    Cited by:

    1. Kang, Jiwon & Song, Junmo, 2015. "Robust parameter change test for Poisson autoregressive models," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 14-21.
    2. Abhik Ghosh, 2022. "Robust parametric inference for finite Markov chains," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 118-147, March.
    3. Lee, Sangyeol & Kim, Dongwon & Kim, Byungsoo, 2023. "Modeling and inference for multivariate time series of counts based on the INGARCH scheme," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    4. Byungsoo Kim & Sangyeol Lee, 2020. "Robust estimation for general integer-valued time series models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(6), pages 1371-1396, December.
    5. Song, Junmo & Oh, Dong-hyun & Kang, Jiwon, 2017. "Robust estimation in stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 243-267.

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