| Titre : |
Structural equation modeling: a bayesian approach |
| Type de document : |
livre |
| Auteurs : |
Lee, S.-Y. |
| Editeur : |
Chichester, U.K. : Wiley |
| Année de publication : |
2007 |
| Collection : |
Wiley series in probability and statistics |
| Importance : |
432 p. |
| ISBN/ISSN/EAN : |
978-0-470-02423-2 |
| Note générale : |
Inventaire 2007: Pointé et emprunté le 05/07/2007 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Bayesian statistical decision theory Modeling |
| Résumé : |
Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. |
| Note de contenu : |
Hbk; |
Structural equation modeling: a bayesian approach [livre] / Lee, S.-Y. . - Chichester, U.K. : Wiley, 2007 . - 432 p.. - ( Wiley series in probability and statistics) . ISBN : 978-0-470-02423-2 Inventaire 2007: Pointé et emprunté le 05/07/2007 Langues : Anglais ( eng)
| Mots-clés : |
Bayesian statistical decision theory Modeling |
| Résumé : |
Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. |
| Note de contenu : |
Hbk; |
|  |
Structural equation modeling: a bayesian approach (2007)
