Title :
Geometrical and Combinatorial Nature of Pearson Residuals
Author :
Tsumoto, Shusaku ; Hirano, Shoji
Author_Institution :
Dept. of Med. Inf., Shimane Univ., Izumo, Japan
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;
Conference_Titel :
Granular Computing (GrC), 2010 IEEE International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-7964-1
DOI :
10.1109/GrC.2010.96
