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Informationen zum Autor Jeffrey S. Simonoff is Professor of Statistics at the NYU Stern School of Business. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He is author or coauthor of roughly 100 articles and five books on the theory and applications of statistics. Brian D. Marx is a Professor of Statistics at Louisiana State University. His main research interests include smoothing, ill-conditioned regression problems, high-dimensional chemometric applications; and he has numerous publications on these topics. He is past president of the Statistical Modelling Society , and is currently member of the Executive Committee of this same international professional society. He is coauthor of the book Regression: Models, Methods, and Applications , as well as, the co-editor of the Sage Handbook on Multilevel Modelling . Klappentext Leading contributors combine practical pieces with overviews of the state of the art in the field, making this handbook essential reading for any student or researcher looking to apply multilevel techniques in their own research Zusammenfassung Leading contributors combine practical pieces with overviews of the state of the art in the field! making this handbook essential reading for any student or researcher looking to apply multilevel techniques in their own research Inhaltsverzeichnis Notes on Contributors Preface Multilevel Modeling - Jeffrey S Simonoff, Marc A Scott and Brian D Marx PART ONE: MULTILEVEL MODEL SPECIFICATION AND INFERENCE The Multilevel Model Framework - Jeff Gill and Andrew Womack Multilevel Model Notation - Establishing the Commonalities - Marc A Scott, Patrick E Shrout and Sharon L Weinberg Likelihood Estimation in Multilevel Models - Harvey Goldstein Bayesian Multilevel Models - Ludwig Fahrmeir, Thomas Kneib, and Stefan Lang The Choice between Fixed and Random Effects - Zac Townsend,Jack Buckley, Masataka Harada and Marc A Scott Centering Predictors and Contextual Effects - Craig K Enders Model Selection for Multilevel Models - Russell Steele Generalized Linear Mixed Models - Overview - Geert Verbeke and Geert Molenberghs Longitudinal Data Modeling - Nan M Laird and Garrett M Fitzmaurice Complexities in Error Structures Within Individuals - Vicente Núnez-Antón and Dale L Zimmerman Design Considerations in Multilevel Studies - Gerard van Breukelen and Mirjam Moerbeek Multilevel Models and Causal Inference - Jennifer Hill PART TWO: VARIATIONS AND EXTENSIONS OF THE MULTILEVEL MODEL Multilevel Functional Data Analysis - Ciprian M Crainiceanu, Brian S Caffo and Jeffrey S Morris Nonlinear Models - Lang Wu and Wei Liu Generalized Linear Mixed Models: Estimation and Inference - Charles E McCulloch and John M Neuhaus Categorical Response Data - Jeroen Vermunt Smoothing and Semiparametric Models - Jin-Ting Zhang Penalized Splines and Multilevel Models - Göran Kauermann and Torben Kuhlenkasper Hierarchical Dynamic Models - Marina Silva Paez and Dani Gamerman Mixture and Latent Class Models in Longitudinal and Other Settings - Ryan P Browne and Paul D McNicholas Multivariate Response Data - Helena Geys and Christel Faes PART THREE: PRACTICAL CONSIDERATIONS IN MODEL FIT AND SPECIFICATION Robust Methods for Multilevel Analysis - Joop Hox and Rens van de Schoot Missing Data - Geert Molenberghs and Geert Verbeke Lack of Fit, Graphics, and M...
