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Auteur Frank E. Harrell Jr. |
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Titre : Regression Modeling Strategies : With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis Type de document : Monographie Auteurs : Frank E. Harrell Jr., Auteur Editeur : Springer International Publishing Année de publication : 2015 Importance : 582 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-3-319-19425-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] Bootstrap (statistique)
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] R (langage)
[Termes IGN] régression linéaire
[Termes IGN] régression logistiqueRésumé : (éditeur) This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. Note de contenu : Introduction
1- General Aspects of Fitting Regression Models
2- Missing Data
3- Multivariable Modeling Strategies
4- Describing, Resampling, Validating, and Simplifying the Model
5- R Software
6- Modeling Longitudinal Responses using Generalized Least Squares
7- Case Study in Data Reduction
8- Overview of Maximum Likelihood Estimation
9- Binary Logistic Regression
10- Case Study in Binary Logistic Regression, Model Selection and Approximation: Predicting Cause of Death
11- Logistic Model Case Study 2: Survival of Titanic Passengers
12- Ordinal Logistic Regression
13- Case Study in Ordinal Regression, Data Reduction, and Penalization
14- Regression Models for Continuous Y and Case Study in Ordinal Regression
15- Transform-Both-Sides Regression
16- Introduction to Survival Analysis
17- Parametric Survival Models
18- Case Study in Parametric Survival Modeling and Model Approximation
19- Cox Proportional Hazards Regression Model
20- Case Study in Cox RegressionNuméro de notice : 25813 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-19425-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95076