EFFICIENCY OF A SEQUENTIAL DENSITY ESTIMATOR UNDER AUTOREGRESSIVE DEPENDENCE MODEL

Authors

  • A. K. Hosni Department of Mathematics, Faculty of Science, Banha University, Banha, EGYPT.
  • M. M. El-Fahham Department of Mathematics, Faculty of Science, Cairo University, Giza, EGYPT.

DOI:

https://doi.org/10.5556/j.tkjm.21.1990.4666

Keywords:

Probability density estimation, Stationary processes, Fourier integral estimator

Abstract

Using kernel estimates of Yamato type the effect of dependent observations is studied. The mean integreated square error of the Fourier integral estimator is considered.

References

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Parzen, E. On estimation of a probability density function and mode. Ann. Math. Stat. V. 33, 8, (1962), 1065-1076.

Prasad, B. and Singh, R.S. Nonparametric kemel estimates of a density function along with its derivatives. Colloquia Math. Soc. Janos, Bolyai, Nonparametric Stat. Inference Budapest, (1980).

Rosenblatt, M. Remarks on some nonparametric estimates of a density function, Ann. Math. Stat. 27, (1956), 832-837.

Wegman, E.J. and Davies, M.I. Remarks on some recursive estimators of probability density. Ann. of Stat., 7, (1979), 316-327.

Wolverton, C. and Wanger, T. Recursive estimatesof probability density. IEEE Trans. Syst. Sci. Cyberent, 5, (1969), 246-257.

Yamato, H. Sequential estimation of a continous probability density function and mode. Bulletin of Math. Stat., 14, (1971), 1-2.

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Published

1990-09-01

How to Cite

Hosni, A. K., & El-Fahham, M. M. . (1990). EFFICIENCY OF A SEQUENTIAL DENSITY ESTIMATOR UNDER AUTOREGRESSIVE DEPENDENCE MODEL. Tamkang Journal of Mathematics, 21(3), 223–231. https://doi.org/10.5556/j.tkjm.21.1990.4666

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Section

Papers