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Density Estimation For Clustered Data

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  • Robert Breunig

Abstract

The commonly used survey technique of clustering introduces dependence into sample data. Such data is frequently used in economic analysis, though the dependence induced by the sample structure of the data is often ignored. In this paper, the effect of clustering on the non-parametric, kernel estimate of the density, f(x), is examined. The window width commonly used for density estimation for the case of i.i.d. data is shown to no longer be optimal. A new optimal bandwidth using a higher-order kernel is proposed and is shown to give a smaller integrated mean squared error than two window widths which are widely used for the case of i.i.d. data. Several illustrations from simulation are provided.

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File URL: http://www.tandfonline.com/doi/abs/10.1081/ETC-100104939
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 20 (2001)
Issue (Month): 3 ()
Pages: 353-367

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Handle: RePEc:taf:emetrv:v:20:y:2001:i:3:p:353-367

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Related research

Keywords: Bandwidth choice; Cluster sampling; Dependent data; Kernel density estimation;

References

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  1. Hardle, W., 1992. "Applied Nonparametric Methods," Papers 9206, Tilburg - Center for Economic Research.
  2. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
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Cited by:
  1. Daniel J. Henderson & Christopher F. Parmeter & R. Robert Russell, 2008. "Modes, weighted modes, and calibrated modes: evidence of clustering using modality tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 607-638.
  2. Breunig, Robert, 2008. "Nonparametric density estimation for stratified samples," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 2194-2200, October.

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