Ronald Christensen

Log-Linear Models and Logistic Regression (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book will serve as a reference and a graduate textbook in the area of categorical data analysis. It includes data sets from engineering, education, sociology, and medicine. Professor Christensen is the successful author of four graduate-level texts.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer US
• Seitenzahl: 484
• Erscheinungstermin: 6. April 2006
• Englisch
• ISBN-13: 9780387226248
• Artikelnr.: 43999491

Autorenporträt

Ronald Christensen is a Distinguished Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of statistical models having linear structure. In addition to numerous technical articles, he is the author of  Plane Answers to Complex Questions:  The Theory of Linear Models; Advanced Linear Modeling:  Statistical Learning and Dependent Data; Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data and coauthor of Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians. Dr. Christensen is a fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics.  His is a past editor of The American Statistician and a past chair of the ASA's Section on Bayesian Statistical Science.

Inhaltsangabe

Two-Dimensional Tables and Simple Logistic Regression.- Three-Dimensional Tables.- Logistic Regression, Logit Models, and Logistic Discrimination.- Independence Relationships and Graphical Models.- Model Selection Methods and Model Evaluation.- Models for Factors with Quantitative Levels.- Fixed and Random Zeros.- Generalized Linear Models.- The Matrix Approach to Log-Linear Models.- The Matrix Approach to Logit Models.- Maximum Likelihood Theory for Log-Linear Models.- Bayesian Binomial Regression. Exact Conditional Tests. - Correspondence Analysis.