Regression with I-priors

Wicher P Bergsma

Econometrics and Statistics, 2020, vol. 14, issue C, 89-111

Abstract: The problem of estimating a parametric or nonparametric regression function in a model with normal errors is considered. For this purpose, a novel objective prior for the regression function is proposed, defined as the distribution maximizing entropy subject to a suitable constraint based on the Fisher information on the regression function. The prior is named I-prior. For the present model, it is Gaussian with covariance kernel proportional to the Fisher information, and mean chosen a priori (e.g., 0).

Keywords: Classification; Empirical bayes; Fisher information; Functional data analysis; g-prior; Maximum entropy; Nonparametric regression; Objective prior; Regression; Reproducing kernel; RKHS; Tikhonov regularization (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:14:y:2020:i:c:p:89-111

DOI: 10.1016/j.ecosta.2019.10.002

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