Henseler Jorg (53 results)

gebundene Ausgabe. Condition: Gut. 798 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 1355.

Hardcover. Condition: Gut. 812 pp. Well preserved copy with only slight signs of wear 329 Sprache: Englisch Gewicht in Gramm: 1020.

Condition: As New. Unread book in perfect condition.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Hardcover. Condition: new. Hardcover. This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Joerg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importanceperformance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides. This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: Brand New. 364 pages. 10.00x7.50x1.00 inches. In Stock.

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Condition: New.

Hardcover. Condition: new. Hardcover. This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Joerg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importanceperformance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides. This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condition: Neu. Neuware - This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Jörg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importance-performance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides.

Hardcover. Condition: new. Hardcover. This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Joerg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importanceperformance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides. This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condition: Like New. Like New; Hardcover; This book is brand new and still sealed in the publisher's original shrinkwrap; Corners are slightly "bumped" through the plastic; This book will be stored and delivered in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); Yellow and orange covers with title in white and orange lettering; 2010, Springer-Verlag Publishing; 798 pages; "Handbook of Partial Least Squares: Concepts, Methods and Applications (Springer Handbooks of Computational Statistics)," by Vincenzo Esposito Vinzi, et al.

Condition: Gut. Zustand: Gut | Seiten: 812 | Sprache: Englisch | Produktart: Bücher | Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O. A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where żsoft models and soft datä were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value. Theauthorwasfortunatetowitnessthedevelopment rsthandforafewyears. Herman Wold suggested (in 1977) to write a PhD-thesis on LISREL versus PLS in the context of latent variable models, more speci cally of żthe basic designż. I was invited to his research team at the Wharton School, Philadelphia, in the fall of 1977. Herman Wold also honoured me by serving on my PhD-committee as a distinguished and decisive member. The thesis was nished in 1981. While I moved into another direction (speci cation, estimation and statistical inference in the c- text of model uncertainty) PLS sprouted very fruitfully in many directions, not only as regards theoretical extensions and innovations (multilevel, nonlinear extensions et cetera) but also as regards applications, notably in chemometrics, marketing, and political sciences. The PLS regression oriented methodology became part of main stream statistical analysis, as can be gathered from references and discussions in important books and journals. See e. g. Hastie et al. (2001), or Stone and Brooks (1990),Frank and Friedman (1993),Tenenhauset al. (2005),there are manyothers.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 812 | Sprache: Englisch | Produktart: Bücher | Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O. A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where żsoft models and soft datä were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value. Theauthorwasfortunatetowitnessthedevelopment rsthandforafewyears. Herman Wold suggested (in 1977) to write a PhD-thesis on LISREL versus PLS in the context of latent variable models, more speci cally of żthe basic designż. I was invited to his research team at the Wharton School, Philadelphia, in the fall of 1977. Herman Wold also honoured me by serving on my PhD-committee as a distinguished and decisive member. The thesis was nished in 1981. While I moved into another direction (speci cation, estimation and statistical inference in the c- text of model uncertainty) PLS sprouted very fruitfully in many directions, not only as regards theoretical extensions and innovations (multilevel, nonlinear extensions et cetera) but also as regards applications, notably in chemometrics, marketing, and political sciences. The PLS regression oriented methodology became part of main stream statistical analysis, as can be gathered from references and discussions in important books and journals. See e. g. Hastie et al. (2001), or Stone and Brooks (1990),Frank and Friedman (1993),Tenenhauset al. (2005),there are manyothers.

Condition: New. pp. 814.

hardcover. Condition: New. In shrink wrap. Looks like an interesting title!

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. In English.

Condition: New. In.

Hardcover. Condition: gut. 2010. Handbook of Partial Least Squares In deutscher Sprache. pages.

Condition: New.

Condition: As New. Unread book in perfect condition.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O. A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where 'soft models and soft data' were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value. Theauthorwasfortunatetowitnessthedevelopment rsthandforafewyears. Herman Wold suggested (in 1977) to write a PhD-thesis on LISREL versus PLS in the context of latent variable models, more speci cally of 'the basic design'. I was invited to his research team at the Wharton School, Philadelphia, in the fall of 1977. Herman Wold also honoured me by serving on my PhD-committee as a distinguished and decisive member. The thesis was nished in 1981. While I moved into another direction (speci cation, estimation and statistical inference in the c- text of model uncertainty) PLS sprouted very fruitfully in many directions, not only as regards theoretical extensions and innovations (multilevel, nonlinear extensions et cetera) but also as regards applications, notably in chemometrics, marketing, and political sciences. The PLS regression oriented methodology became part of main stream statistical analysis, as can be gathered from references and discussions in important books and journals. See e. g. Hastie et al. (2001), or Stone and Brooks (1990),Frank and Friedman (1993),Tenenhauset al. (2005),there are manyothers.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O. A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where 'soft models and soft data' were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value. Theauthorwasfortunatetowitnessthedevelopment rsthandforafewyears. Herman Wold suggested (in 1977) to write a PhD-thesis on LISREL versus PLS in the context of latent variable models, more speci cally of 'the basic design'. I was invited to his research team at the Wharton School, Philadelphia, in the fall of 1977. Herman Wold also honoured me by serving on my PhD-committee as a distinguished and decisive member. The thesis was nished in 1981. While I moved into another direction (speci cation, estimation and statistical inference in the c- text of model uncertainty) PLS sprouted very fruitfully in many directions, not only as regards theoretical extensions and innovations (multilevel, nonlinear extensions et cetera) but also as regards applications, notably in chemometrics, marketing, and political sciences. The PLS regression oriented methodology became part of main stream statistical analysis, as can be gathered from references and discussions in important books and journals. See e. g. Hastie et al. (2001), or Stone and Brooks (1990),Frank and Friedman (1993),Tenenhauset al. (2005),there are manyothers.

Hardcover. Condition: Brand New. 850 pages. 9.49x6.57x1.89 inches. In Stock.