OSMODとその拡張:人工データによる特性の検討
1993 年 20 巻 2 号 p. 9-23
詳細
1993 年 20 巻 2 号 p. 9-23
OSMOD(Saito&Otsu, 1988), which is a principal component analysis for mixed mesurement data, and its extensions are exarnined with applications to artificial data and analytical considerations. Two important conclusions are obtained.(1)In many cases, Minimizing Generalized Variance criterion(MGV)works better than Maximizing Total Variance criterion(MTV).(2)OSMOD gives more stable category scores than Multiple Correspondence Analysis(MCA).
