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Quick multivariate kernel density estimation for massive data sets

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  • K. F. Cheng
  • C. K. Chu
  • Dennis K. J. Lin

Abstract

Massive data sets are becoming popular in this information era. Due to the limitation of computer memory space and the computing time, the kernel density estimation for massive data sets, although strongly demanding, is rather challenging. In this paper, we propose a quick algorithm for multivariate density estimation which is suitable for massive data sets. The term quick is referred to indicate the computing ease. Theoretical properties of the proposed algorithm are developed. Its empirical performance is demonstrated through a credit card example and numerous simulation studies. It is shown that in addition to its computational ease, the proposed algorithm is as good as the traditional methods (for the situations where these traditional methods are feasible). Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • K. F. Cheng & C. K. Chu & Dennis K. J. Lin, 2006. "Quick multivariate kernel density estimation for massive data sets," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(5‐6), pages 533-546, September.
    • Handle: RePEc:wly:apsmbi:v:22:y:2006:i:5-6:p:533-546
    DOI: 10.1002/asmb.642
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    Cited by:

    1. Jiecheng Wang & Yantong Liu & Jincai Chang, 2022. "An Improved Model for Kernel Density Estimation Based on Quadtree and Quasi-Interpolation," Mathematics, MDPI, vol. 10(14), pages 1-15, July.

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