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PRIM analysis

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  • Polonik, Wolfgang
  • Wang, Zailong

Abstract

This paper analyzes a data mining/bump hunting technique known as PRIM [1]. PRIM finds regions in high-dimensional input space with large values of a real output variable. This paper provides the first thorough study of statistical properties of PRIM. Amongst others, we characterize the output regions PRIM produces, and derive rates of convergence for these regions. Since the dimension of the input variables is allowed to grow with the sample size, the presented results provide some insight about the qualitative behavior of PRIM in very high dimensions. Our investigations also reveal some shortcomings of PRIM, resulting in some proposals for modifications.

Suggested Citation

  • Polonik, Wolfgang & Wang, Zailong, 2010. "PRIM analysis," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 525-540, March.
    • Handle: RePEc:eee:jmvana:v:101:y:2010:i:3:p:525-540
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    References listed on IDEAS

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    1. Ursula Becker & Ludwig Fahrmeir, 2001. "Bump Hunting for Risk: a New Data Mining Tool and its Applications," Computational Statistics, Springer, vol. 16(3), pages 373-386, September.
    2. Polonik, Wolfgang, 1997. "Minimum volume sets and generalized quantile processes," Stochastic Processes and their Applications, Elsevier, vol. 69(1), pages 1-24, July.
    3. Burman, Prabir & Polonik, Wolfgang, 2009. "Multivariate mode hunting: Data analytic tools with measures of significance," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1198-1218, July.
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