, Volume 22, Issue 2, pp 237-239
Date: 05 Apr 2013

Comments on: Model-free model-fitting and predictive distributions

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I enjoyed reading this paper, which presents a new look to the problem of developing predictive distributions under very general conditions. The key idea of this new method is transforming the data to iid observations exploiting a semiparametric model (such as a mean-scale one) or the probability integral transformation (PIT) when a completely nonparametric model is preferred under a smoothness class assumption. In this last sense the term model-free prediction is coined by the author. This approach of transformation to iid (pseudo) residuals is parallel to the usual modeling strategy conducted in parametric statistics to isolate the random component in the data from the information contained in the conditioning variables. Then, resampling can be used naturally to construct valid confidence intervals using the empirical distribution of these residuals which are iid by construction. To accommodate (possibly infinite dimensional) parameter estimation effects, one-leave-out transformation ...