Quick multivariate kernel density estimation for massive data sets

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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).

Original languageEnglish (US)
Pages (from-to)533-546
Number of pages14
JournalApplied Stochastic Models in Business and Industry
Volume22
Issue number5-6
DOIs
StatePublished - Sep 1 2006

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Business, Management and Accounting(all)
  • Management Science and Operations Research

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