Practical Robust Estimators for the Imprecise Dirichlet Model

Details Practical Robust Estimators for the Imprecise Dirichlet Model Practical Robust Estimators for the Imprecise Dirichlet Model

2009-01-26

arxiv.org/abs/0901.4137v1

International Journal of Approximate Reasoning, 50:2 (2009) pages 231-242

Mathematics

Statistics Theory

22 pages, 2 figures

Scientific paper

Walley's Imprecise Dirichlet Model (IDM) for categorical i.i.d. data extends the classical Dirichlet model to a set of priors. It overcomes several fundamental problems which other approaches to uncertainty suffer from. Yet, to be useful in practice, one needs efficient ways for computing the imprecise=robust sets or intervals. The main objective of this work is to derive exact, conservative, and approximate, robust and credible interval estimates under the IDM for a large class of statistical estimators, including the entropy and mutual information.

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