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Varying kernel marginal density estimator for a positive time series

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  • N. Balakrishna
  • Hira L. Koul

Abstract

This paper analyses the large sample behaviour of a varying kernel density estimator of the marginal density of a non-negative stationary and ergodic time series that is also strongly mixing. In particular we obtain an approximation for bias, mean square error and establish asymptotic normality of this density estimator. We also derive an almost sure uniform consistency rate over bounded intervals of this estimator. A finite sample simulation shows some superiority of the proposed density estimator over the one based on a symmetric kernel.

Suggested Citation

  • N. Balakrishna & Hira L. Koul, 2017. "Varying kernel marginal density estimator for a positive time series," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 531-552, July.
    • Handle: RePEc:taf:gnstxx:v:29:y:2017:i:3:p:531-552
    DOI: 10.1080/10485252.2017.1324968
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    References listed on IDEAS

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