Dynamic model selection and analysis based on the distribution of power series

Author

Deqiang Chen

Author_Institution

Dept. of Inf. Sci. & Technol., East China Univ. of Political Sci. & Law, Shanghai, China

fYear

2013

fDate

20-22 Dec. 2013

Firstpage

2486

Lastpage

2489

Abstract

In this paper, the traditional likelihood ratio test (LRT) method has been improved. The relative entropy density deviation (REDD) method was introduced for the distribution of power series dynamic models. Through small amounts of data analysis and comparison, the mathematical models can be quickly established based on the collected data. Experiments´ results show that this method can be used to dynamically select the Poisson distribution model, the binomial distribution model, the negative binomial model and so on.

Keywords

Poisson distribution; binomial distribution; data analysis; entropy; LRT method; Poisson distribution model; REDD method; binomial distribution model; data analysis; dynamic model selection; likelihood ratio test; power series; relative entropy density deviation; Analytical models; Biological system modeling; Computational modeling; Data models; Educational institutions; Entropy; Mathematical model; distribution of power series; likelihood ratio test; relative entropy density deviation;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on

Conference_Location

Shengyang

Print_ISBN

978-1-4799-2564-3

Type

conf

DOI

10.1109/MEC.2013.6885454

Filename

6885454