EFFICIENCY OF A SEQUENTIAL DENSITY ESTIMATOR UNDER AUTOREGRESSIVE DEPENDENCE MODEL
DOI:
https://doi.org/10.5556/j.tkjm.21.1990.4666Keywords:
Probability density estimation, Stationary processes, Fourier integral estimatorAbstract
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
Deheuvel's, P. Estimation de la densite. Doctorat d'Etat, Universite Paris VI, (K), (1974).
Hart, J.D. Efficiency of kernel density estimator under an autoregressive dependence model. J. Amer. Stat. Ass. Vol Vol. 79,385, (1984), 110-117.
Menon, V.V., Prased, B. and Singh, R.S. Non-parametric recursive estimates of a probability density function and its derivatives. J. Stat. Planing and Inference. 9, (1983), 71-82.
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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