Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space. This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because...
Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic...
This valuable reference on projectors, generalized inverses, and SVD covers concepts numerous cutting-edge concepts and provides systematic and in-depth accounts of these ideas from the viewpoint of linear transformations of finite dimensional vector spaces.
This valuable reference on projectors, generalized inverses, and SVD covers concepts numerous cutting-edge concepts and provides systematic and in-dep...
At the International Meeting of the Psychometric Society in Osaka, Japan, more than 300 participants from 19 countries gathered to discuss recent developments in the theory and application of psychometrics. This volume of proceedings includes papers on methods of psychometrics such as the structural equation model and item response theory. The book is in eight major sections: keynote speeches and invited lectures; structural equation modeling and factor analysis; IRT and adaptive testing; multivariate statistical methods; scaling; classification methods; and independent and principal...
At the International Meeting of the Psychometric Society in Osaka, Japan, more than 300 participants from 19 countries gathered to discuss recent d...
