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A Rapid Two‐Stage Modelling Technique for Exploring Large Data Sets

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  • W. R. Gilks

Abstract

A two‐stage technique for rapidly exploring moderate or large data sets is proposed. At the first stage a model is fitted by Maximum Likelihood Estimation (MLE) to each of one or more data segments. The results are then used repeatedly to explore relationships within and between segments by means of second‐stage models specified in terms of segment variates. Special cases include variable selection and parallel regressions (Ross, 1980). With large data sets the parameter estimates can be obtained very rapidly, and closely resemble MLEs, as illustrated by kidney transplant survival data.

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  • W. R. Gilks, 1986. "A Rapid Two‐Stage Modelling Technique for Exploring Large Data Sets," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(2), pages 183-194, June.
    • Handle: RePEc:bla:jorssc:v:35:y:1986:i:2:p:183-194
    DOI: 10.2307/2347269
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    Cited by:

    1. Jovanovic, Borko D. & Hosmer, David W. & Buonaccorsi, John P., 1995. "Equivalence of several methods for efficient best subsets selection in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 20(1), pages 59-64, July.

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