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Monte Carlo conditioning on a sufficient statistic

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  • Bo Henry Lindqvist
  • Gunnar Taraldsen

Abstract

In this paper we derive general formulae suitable for Monte Carlo computation of conditional expectations of functions of a random vector given a sufficient statistic. The problem of direct sampling from the conditional distribution is considered in particular. It is shown that this can be done by a simple parameter adjustment of the original statistical model, provided the model has a certain pivotal structure. A connection with a classical problem regarding fiducial and posterior distributions is pointed out. Copyright 2005, Oxford University Press.

Suggested Citation

  • Bo Henry Lindqvist & Gunnar Taraldsen, 2005. "Monte Carlo conditioning on a sufficient statistic," Biometrika, Biometrika Trust, vol. 92(2), pages 451-464, June.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:2:p:451-464
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    File URL: http://hdl.handle.net/10.1093/biomet/92.2.451
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    Cited by:

    1. José González-Barrios & Federico O’Reilly & Raúl Rueda, 2010. "Durbin’s random substitution and conditional Monte Carlo," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(3), pages 369-383, November.
    2. Andrea C. Garcia‐Angulo & Gerda Claeskens, 2023. "Exact uniformly most powerful postselection confidence distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 358-382, March.
    3. Bo H. Lindqvist & Rasmus Erlemann & Gunnar Taraldsen, 2022. "Conditional Monte Carlo revisited," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 943-968, September.

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