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On the Asymptotics of Quantizers in Two Dimensions

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  • Su, Yingcai
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    When the mean square distortion measure is used, asymptotically optimal quantizers of uniform bivariate random vectors correspond to the centers of regular hexagons (Newman, 1982), and if the random vector is non-uniform, asymptotically optimal quantizers are the centers of piecewise regular hexagons where the sizes of the hexagons are determined by a properly chosen density function (Su and Cambanis, 1996). This paper considers bivariate random vectors with finite[gamma]th ([gamma]>0) moment. If the[gamma]th mean distortion measure is used, a complete characterization of the asymptotically optimal quantizers is given. Furthermore, it is shown that the procedure introduced by Su and Cambanis (1996) is also asymptotically optimal for every[gamma]>0. Examples with a normal distribution and a Pearson type VII distribution are considered.

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

    Volume (Year): 61 (1997)
    Issue (Month): 1 (April)
    Pages: 67-85

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    Handle: RePEc:eee:jmvana:v:61:y:1997:i:1:p:67-85
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    1. Tarpey, T., 1995. "Principal Points and Self-Consistent Points of Symmetrical Multivariate Distributions," Journal of Multivariate Analysis, Elsevier, vol. 53(1), pages 39-51, April.
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