Model specification via sequential coherence and backward induction

P. Richard Hahn

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arXiv · 1502.06045

Model specification via sequential coherence and backward induction

P. Richard Hahn

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Abstract

This paper describes how to specify probability models for data analysis via a backward induction procedure. The new approach yields coherent, prior-free uncertainty assessment. After presenting some intuition-building examples, the new approach is applied to a kernel density estimator, which leads to a novel method for computing point-wise credible intervals in nonparametric density estimation. The new approach has two additional advantages; 1) the posterior mean density can be accurately approximated without resorting to Monte Carlo simulation and 2) concentration bounds are easily established as a function of sample size.

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P. Richard Hahn. 2015-02-21. Model specification via sequential coherence and backward induction. https://arxiv.org/abs/1502.06045

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