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Density estimation from streaming data using wavelets

In: Compstat 2006 - Proceedings in Computational Statistics

Author

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  • Edward J. Wegman

    (George Mason University)

  • Kyle A. Caudle

    (United States Naval Academy)

Abstract

In this paper we discuss approaches to estimating probability densities from streaming data based on wavelets. It is expected that streaming datasets are large and that the rate of data acquisition is very high. Thus it is not possible to recompute the entire density so that recursive algorithms are necessary. In addition, because streaming data are typically not stationary, older data in the stream are usually less valuable. It is, therefore, necessary to discount older data. We develop in this paper a methodology that is applicable to any orthonormal bases, but, in particular, a methodology for wavelet bases.

Suggested Citation

  • Edward J. Wegman & Kyle A. Caudle, 2006. "Density estimation from streaming data using wavelets," Springer Books, in: Alfredo Rizzi & Maurizio Vichi (ed.), Compstat 2006 - Proceedings in Computational Statistics, pages 231-242, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-1709-6_18
    DOI: 10.1007/978-3-7908-1709-6_18
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