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Density Estimation For Clustered Data

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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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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  1. Wolfgang Hardle & Oliver Linton, 1994. "Applied Nonparametric Methods," Cowles Foundation Discussion Papers 1069, Cowles Foundation for Research in Economics, Yale University.
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