Interval Estimation of Data Series with Poor Information Using Fuzzy Norm Method

Author

Xia, Xintao ; Zhou, Qing

Author_Institution

Coll. of Mechatronical Eng., Henan Univ. of Sci. & Technol., Luoyang, China

fYear

2009

fDate

26-28 Dec. 2009

Firstpage

158

Lastpage

163

Abstract

Based on the fuzzy set theory and the norm theory, the fuzzy norm method is proposed to solve some of problems about interval estimation under the condition of poor information system with unknown probability distributions and small samples. By extracting difference value information from data series, the membership function and empirical distribution function are defined with a minimum of the maximum norm, thus the estimated interval of poor information system are obtained, having the over 95% confidence level and the below 15% relative error.

Keywords

fuzzy set theory; parameter estimation; sampling methods; statistical distributions; data series estimation; empirical distribution function; fuzzy norm method; fuzzy set theory; membership function; norm theory; poor information condition; poor information system; small samples; unknown probability distributions; Bayesian methods; Data engineering; Data mining; Distribution functions; Educational institutions; Fuzzy set theory; Fuzzy systems; Information science; Information systems; Probability distribution; fuzzy set; information poor system; interval estimation; norm; random process;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Engineering (ISISE), 2009 Second International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-6325-1

Electronic_ISBN

978-1-4244-6326-8

Type

conf

DOI

10.1109/ISISE.2009.73

Filename

5447167