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An Introduction to Coding Theory and the Two‐Part Minimum Description Length Principle

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  • Thomas C. M. Lee

Abstract

This article provides a tutorial introduction to the so‐called two‐part minimum description length (MDL) principle proposed by Rissanen. This two‐part MDL principle is a powerful methodology for solving many statistical model selection problems. However, it seems that this powerful methodology is only adopted by a small number of statisticians to tackle a small number of problems. One plausible reason for this is that the coding theory results required by the MDL principle are somewhat new to most statisticians, and that there are not many readily accessible articles introducing these results appearing in the statistical literature. The first part of this article is devoted to a discussion of such coding theory results. Then, in the second part of the article, the two‐part MDL principle is introduced and explained. In doing so, only those coding theory results that are presented in the first part of the article are used. Finally, the applicability of the two‐part MDL principle is demonstrated by applying it to tackle four different statistical problems. Cet article prévoit une introduction d' instruction au principe minium de la longueur de description (MDL) proposé par Rissanen. Ce principe de MDL est une méthodologie puissante pour résoudre beaucoup de problémes modéles statistiques de sélection. Cependant, il semble que cette méthodologi puissante est seulement adoptée par un nombre restreint de statisticiens pour aborder un nombre restreint de problémes. Une raison plausible de ceci est que les résultats de théorie de codage exigément accessibles présentant ces résultats apparaissant dans la littéstatistique. La premiére partie de cet article east consacrée à unediscussion de tels résultats de théorie de codage. Puis, dans la deuxiéme partie de I'article,leprinciple de MDL est pré sente et expliqueé.De cette maniére, sculement ceux des résultants de théorie de codage présentés dans la premiére partie de I' artical sont utilisés. En conclusion, I applicabilité du principe de MDL est déen s' montré en s' appliquant I' aux probleémes statistiques differents de I' artical quatre.

Suggested Citation

  • Thomas C. M. Lee, 2001. "An Introduction to Coding Theory and the Two‐Part Minimum Description Length Principle," International Statistical Review, International Statistical Institute, vol. 69(2), pages 169-183, August.
  • Handle: RePEc:bla:istatr:v:69:y:2001:i:2:p:169-183
    DOI: 10.1111/j.1751-5823.2001.tb00455.x
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    Cited by:

    1. Alexander Aue & Rex C. Y. Cheung & Thomas C. M. Lee & Ming Zhong, 2014. "Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1241-1256, September.
    2. Chun Yip Yau & Chong Man Tang & Thomas C. M. Lee, 2015. "Estimation of Multiple-Regime Threshold Autoregressive Models With Structural Breaks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1175-1186, September.
    3. Robert A. Stine, 2004. "Model Selection Using Information Theory and the MDL Principle," Sociological Methods & Research, , vol. 33(2), pages 230-260, November.

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