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Finite sample performance of density estimators from unequally spaced data

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  • Vilar, José A.
  • Vilar, Juan M.

Abstract

For broad classes of deterministic and random sampling schemes {[tau]k}, exact mean integrated squared error (MISE) expressions for the kernel estimator of the marginal density of a first-order continuous-time autoregressive process are derived. The obtained expressions show that the effect on MISE due to both the sampling scheme and the sampling rate is significant for finite samples. The results are also extended to a case where the irregular observations are generated from a mixture of first-order continuous-time processes.

Suggested Citation

  • Vilar, José A. & Vilar, Juan M., 2000. "Finite sample performance of density estimators from unequally spaced data," Statistics & Probability Letters, Elsevier, vol. 50(1), pages 63-73, October.
    • Handle: RePEc:eee:stapro:v:50:y:2000:i:1:p:63-73
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    References listed on IDEAS

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    1. J. Vilar, 1995. "Kernel estimation of the regression function with random sampling times," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 4(1), pages 137-178, June.
    2. Robinson, P. M., 1977. "Estimation of a time series model from unequally spaced data," Stochastic Processes and their Applications, Elsevier, vol. 6(1), pages 9-24, November.
    3. Mielniczuk, Jan, 1997. "On the asymptotic mean integrated squared error of a kernel density estimator for dependent data," Statistics & Probability Letters, Elsevier, vol. 34(1), pages 53-58, May.
    4. Wand, M. P., 1992. "Finite sample performance of density estimators under moving average dependence," Statistics & Probability Letters, Elsevier, vol. 13(2), pages 109-115, January.
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