Latent Variable Path Modeling with Partial Least SquaresSpringer Science & Business Media, 11 Nov 2013 - 286 halaman Partial Least Squares (PLS) is an estimation method and an algorithm for latent variable path (LVP) models. PLS is a component technique and estimates the latent variables as weighted aggregates. The implications of this choice are considered and compared to covariance structure techniques like LISREL, COSAN and EQS. The properties of special cases of PLS (regression, factor scores, structural equations, principal components, canonical correlation, hierarchical components, correspondence analysis, three-mode path and component analysis) are examined step by step and contribute to the understanding of the general PLS technique. The proof of the convergence of the PLS algorithm is extended beyond two-block models. Some 10 computer programs and 100 applications of PLS are referenced. The book gives the statistical underpinning for the computer programs PLS 1.8, which is in use in some 100 university computer centers, and for PLS/PC. It is intended to be the background reference for the users of PLS 1.8, not as textbook or program manual. |
Isi
| 11 | |
The Basic and the Extended PLS Method | 27 |
Foundations of Partial Least Squares 63 | 62 |
Mixed Measurement Level Multivariate Data | 155 |
PLS vs ML | 199 |
Latent Variables ThreeMode Path LVP3 Analysis | 227 |
PLS Programs and Applications | 241 |
| 249 | |
| 273 | |
Edisi yang lain - Lihat semua
Latent Variable Path Modeling with Partial Least Squares Jan-Bernd Lohmöller Pratinjau tidak tersedia - 2014 |
Latent Variable Path Modeling with Partial Least Squares Jan-Bernd Lohmöller Pratinjau tidak tersedia - 2013 |
Istilah dan frasa umum
aggregate analysis blocks Boolean variables canonical correlation analysis Canonical Correlation model categorical variables causal component model computed conditional expectation contingency table convergence correlation matrix cov(x cov(y covariance matrix data matrix denoted design matrix diagonal eigenvalue eigenvector equation factor model Herman Wold identical inner model inner weights inside approximation iteration cycle Kronecker latent variables Least Squares linear LISREL Lohmöller LV correlation LV path LVP model manifest variables method ModeA ModeB multiple correlation multiple regression orthogonal Partial Least Squares path coefficients path model PLS algorithm PLS estimates PLS model predictand prediction predictor specification principal component product matrix relation residual covariance residual variables restrictions sample scale solution split step structure model super contingency table theoretical three-mode uncorrelated values variance weighting scheme Wold zero πη πρ πρπ Σω