@inproceedings{a7f097a8164949e89b8c211b0546c851,

title = "An entropy estimator based on polynomial regression with poisson error structure",

abstract = "A method for estimating Shannon differential entropy is proposed based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Polynomial regression with Poisson error structure is utilized to estimate the values of density function. The density estimates at every given data points are averaged to obtain entropy estimators. The proposed estimator is shown to perform well through numerical experiments for various probability distributions.",

keywords = "Density estimation, Entropy, Poisson error structure, Regression",

author = "Hideitsu Hino and Shotaro Akaho and Noboru Murata",

note = "Funding Information: Part of this work was supported by JSPS KAKENHI No. 25120009, 25120011, and 16K16108.; 23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46672-9_2",

language = "English",

isbn = "9783319466712",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "11--19",

editor = "Seiichi Ozawa and Kazushi Ikeda and Derong Liu and Akira Hirose and Kenji Doya and Minho Lee",

booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",

}