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A semiparametric density estimator based on elliptical distributions

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  • Liebscher, Eckhard
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    Abstract

    In the paper we study a semiparametric density estimation method based on the model of an elliptical distribution. The method considered here shows a way to overcome problems arising from the curse of dimensionality. The optimal rate of the uniform strong convergence of the estimator under consideration coincides with the optimal rate for the usual one-dimensional kernel density estimator except in a neighbourhood of the mean. Therefore the optimal rate does not depend on the dimension. Moreover, asymptotic normality of the estimator is proved.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 92 (2005)
    Issue (Month): 1 (January)
    Pages: 205-225

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    Handle: RePEc:eee:jmvana:v:92:y:2005:i:1:p:205-225

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    Related research

    Keywords: Elliptical distributions Kernel density estimator;

    References

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    1. Liebscher E., 1998. "On A Class Of Plug-In Methods Of Bandwidth Selection For Kernel Density Estimators," Statistics & Risk Modeling, De Gruyter, vol. 16(3), pages 229-244, March.
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    Cited by:
    1. Heather Battey & Oliver Linton, 2013. "Nonparametric estimation of multivariate elliptic densities via finite mixture sieves," CeMMAP working papers CWP15/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Hallin, Marc & Paindaveine, Davy, 2009. "Optimal tests for homogeneity of covariance, scale, and shape," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 422-444, March.

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