Log Linear Modeling
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Log Linear Modeling
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Author : Alexander von Eye
language : en
Publisher: John Wiley & Sons
Release Date : 2014-08-21
Log Linear Modeling written by Alexander von Eye and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-21 with Mathematics categories.
An easily accessible introduction to log-linear modeling for non-statisticians Highlighting advances that have lent to the topic's distinct, coherent methodology over the past decade, Log-Linear Modeling: Concepts, Interpretation, and Application provides an essential, introductory treatment of the subject, featuring many new and advanced log-linear methods, models, and applications. The book begins with basic coverage of categorical data, and goes on to describe the basics of hierarchical log-linear models as well as decomposing effects in cross-classifications and goodness-of-fit tests. Additional topics include: The generalized linear model (GLM) along with popular methods of coding such as effect coding and dummy coding Parameter interpretation and how to ensure that the parameters reflect the hypotheses being studied Symmetry, rater agreement, homogeneity of association, logistic regression, and reduced designs models Throughout the book, real-world data illustrate the application of models and understanding of the related results. In addition, each chapter utilizes R, SYSTAT®, and §¤EM software, providing readers with an understanding of these programs in the context of hierarchical log-linear modeling. Log-Linear Modeling is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It also serves as an excellent reference for applied researchers in virtually any area of study, from medicine and statistics to the social sciences, who analyze empirical data in their everyday work.
Log Linear Models And Logistic Regression
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Author : Ronald Christensen
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-06
Log Linear Models And Logistic Regression written by Ronald Christensen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-06 with Mathematics categories.
The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. It also carefully examines the differences in model interpretations and evaluations that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM, while numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. Throughoutthe book, the treatment is designed for students with prior knowledge of analysis of variance and regression.
Log Linear Models
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Author : Ronald Christensen
language : en
Publisher:
Release Date : 1990
Log Linear Models written by Ronald Christensen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Applied mathematics categories.
This book examines log-linear models for contingency tables. It uses previous knowledge of analysis of variance and regression to motivate and explicate the use of log-linear models. It is a textbook primarily directed at advanced Masters degree students in statistics but can be used at both higher and lower levels. Outlines for introductory, intermediate and advanced courses are given in the preface. All the fundamental statistics for analyzing data using log-linear models is given.
Log Linear Models And Logistic Regression
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Author : Ronald Christensen
language : en
Publisher: Springer
Release Date : 2013-03-08
Log Linear Models And Logistic Regression written by Ronald Christensen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-08 with Mathematics categories.
The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. It also carefully examines the differences in model interpretations and evaluations that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM, while numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. Throughoutthe book, the treatment is designed for students with prior knowledge of analysis of variance and regression.
Log Linear Models
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Author : Ronald Christensen
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-14
Log Linear Models written by Ronald Christensen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-14 with Mathematics categories.
This book examines log-linear models for contingency tables. Logistic re gression and logistic discrimination are treated as special cases and gener alized linear models (in the GLIM sense) are also discussed. The book is designed to fill a niche between basic introductory books such as Fienberg (1980) and Everitt (1977) and advanced books such as Bishop, Fienberg, and Holland (1975), Haberman (1974), and Santner and Duffy (1989). lt is primarily directed at advanced Masters degree students in Statistics but it can be used at both higher and lower levels. The primary theme of the book is using previous knowledge of analysis of variance and regression to motivate and explicate the use of log-linear models. Of course, both the analogies and the distinctions between the different methods must be kept in mind. The book is written at several levels. A basic introductory course would take material from Chapters I, II (deemphasizing Section II. 4), III, Sec tions IV. 1 through IV. 5 (eliminating the material on graphical models), Section IV. lü, Chapter VII, and Chapter IX. The advanced modeling ma terial at the end of Sections VII. 1, VII. 2, and possibly the material in Section IX. 2 should be deleted in a basic introductory course. For Mas ters degree students in Statistics, all the material in Chapters I through V, VII, IX, and X should be accessible. For an applied Ph. D.
Understanding Log Linear Analysis With Ilog
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Author : Roger Bakeman
language : en
Publisher: Psychology Press
Release Date : 1994
Understanding Log Linear Analysis With Ilog written by Roger Bakeman and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Mathematics categories.
Whenever data are categorical and their frequencies can be arrayed in multidimensional tables, log-linear analysis is appropriate. Like analysis of variance and multiple regression for quantitative data, log-linear analysis lets users ask which main effects and interactions affect an outcome of interest. Until recently, however, log-linear analysis seemed difficult -- accessible only to the statistically motivated and savvy. Designed for students and researchers who want to know more about this extension of the two-dimensional chi-square, this book introduces basic ideas in clear and straightforward prose and applies them to a core of example studies. ILOG -- a software program that runs on IBM compatible personal computers -- is included with this volume. This interactive program lets readers work through and explore examples provided throughout the book. Because ILOG is capable of serious log-linear analyses, readers gain not only understanding, but the means to put that understanding into practice as well.
Log Linear Models
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Author : David Knoke
language : en
Publisher: SAGE
Release Date : 1980-08
Log Linear Models written by David Knoke and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980-08 with Mathematics categories.
Introduces methods for quantitative assessment of relationships among categoric variables in multivariable crosstabulations. Procedures to estimate and interpret effect parameters for hierarchical models are described for both the general loglinear model and its logit version.
Log Linear Models For Event Histories
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Author : Jeroen K. Vermunt
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1997-05-13
Log Linear Models For Event Histories written by Jeroen K. Vermunt and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-05-13 with Mathematics categories.
Event history analysisùa method for explaining why some people are more likely to experience a particular event, transition, or change than other peopleùhas been useful in the social sciences for studying the processes of social change. One of the main difficulties, however, in using this technique is that often information is (partially) missing on some of the relevant variables. Author Jeroen K. Vermunt presents a general approach to these missing data problems in event history analysis that is based on the similarities between log-linear, hazard, and event history models. The book begins with a discussion of log-linear, log-rate, and modified path models and methods for obtaining maximum likelihood estimates of the parameters of these models. Vermunt then shows how to incorporate variables with missing information in log-linear models for non-response. In addition, he covers such topics as the main types of hazard models; censoring; the use of time-varying covariates; models for competing risks; multivariate hazard models; and a general approach for dealing with missing data problems, including unobserved heterogeneity, measurement error in the dependent variable, measurement error in the covariate, partially missing information on the dependent variable, and partially observed covariate values.
Analyzing Qualitative Categorical Data
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Author : Leo A. Goodman
language : en
Publisher: University Press of America
Release Date : 1985
Analyzing Qualitative Categorical Data written by Leo A. Goodman and has been published by University Press of America this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with Mathematics categories.
Abstract: Statistical methods covering log-linear models and latent-structure analysis are presented and described for the analysis of qualitative or categorical data to assist the pressing needs of social researchers and others in developing and applying a unified and systematic approach to the analysis of such data. The scope of applications in this approach includes methods for the sociologist examining the relationship between poverty and crime; the educational researcher examining the reliability and validity of a set of test items; the psychometrician developing a new measurement scale; the market researcher analyzing purchase behavior in different market segments; the medical researcher attempting to identify factors associated with various diseases (e.g., breast cancer); and the political scientist examining voter behavior. The methods are applicable to computer programs. (wz).
Log Linear Models Extensions And Applications
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Author : Aleksandr Aravkin
language : en
Publisher: MIT Press
Release Date : 2018-11-27
Log Linear Models Extensions And Applications written by Aleksandr Aravkin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.
Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications. Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives. Contributors Aleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg