DocumentCode
2143335
Title
Geometrical and Combinatorial Nature of Pearson Residuals
Author
Tsumoto, Shusaku ; Hirano, Shoji
Author_Institution
Dept. of Med. Inf., Shimane Univ., Izumo, Japan
fYear
2010
fDate
14-16 Aug. 2010
Firstpage
489
Lastpage
494
Abstract
This paper focuses on residual analysis of statistical independence of multiple variables from the viewpoint of linear algebra. The results show that multidimensional residuals are represented as linear sum of determinants of 2 × 2 submatrices, which can be viewed as information granules measuring the degree of statistical dependence.
Keywords
artificial intelligence; combinatorial mathematics; information theory; matrix algebra; statistical analysis; Pearson residuals; information granules; linear algebra; multidimensional residuals; statistical independence residual analysis; submatrices; Context; Data mining; Equations; Matrix decomposition; Probability; Pearson residual; contingency table; information granules; statistical independence;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing (GrC), 2010 IEEE International Conference on
Conference_Location
San Jose, CA
Print_ISBN
978-1-4244-7964-1
Type
conf
DOI
10.1109/GrC.2010.96
Filename
5575973
Link To Document