Marginal maximum likelihood estimation of single parameter logistic based on EM algorithm

Author

Sun, Xueyan ; Jing, Fengxuan ; Xie, Xiaoyao ; Zhang, Anyu

Author_Institution

Key Lab. of Inf. & Comput. Sci. of Guizhou Province, Guizhou Normal Univ., Guiyang, China

fYear

2010

Firstpage

173

Lastpage

175

Abstract

Cluster analysis is one of the most important functions of data mining. Expectation Maximization (EM) method is an important technology based on model clustering method. The expectation maximization algorithm is analyzed in this research and applied to Adaptive Testing System, in which logistic function in item response theory serves as a model, and the combination of methods of marginal maximum likelihood estimation (MMLE) and the EM algorithm are used to estimate the difficulty parameter estimation of single-parameter logistic function. This effort achieves good results.

Keywords

data analysis; data mining; expectation-maximisation algorithm; pattern clustering; adaptive testing system; cluster analysis; data mining; expectation maximization algorithm; item response theory; logistic function; marginal maximum likelihood estimation; model clustering method; parameter estimation; single parameter logistic; Adaptation model; Clustering algorithms; Data models; Logistics; Maximum likelihood estimation; Presses; EM algorithm; logistic function; parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Anti-Counterfeiting Security and Identification in Communication (ASID), 2010 International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-6731-0

Type

conf

DOI

10.1109/ICASID.2010.5551506

Filename

5551506