2011/03/24 by Alexis Akira Toda, Toda, Alexis Akira
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Mechanics and Entropy #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.48550/arxiv.1103.4890
Submitted to Econometrica on March 24, 2011
openalex publication_date 2011/03/24 · arxiv created 2011/03/25 · arxiv updated 2011/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper applies the recently axiomatized Optimum Information Principle\n(minimize the Kullback-Leibler information subject to all relevant information)\nto nonparametric density estimation, which provides a theoretical foundation as\nwell as a computational algorithm for maximum entropy density estimation. The\nestimator, called optimum information estimator, approximates the true density\narbitrarily well. As a by-product I obtain a measure of goodness of fit of\nparametric models (both conditional and unconditional) and an absolute\ncriterion for model selection, as opposed to other conventional methods such as\nAIC and BIC which are relative measures.\n