REGRESSION MODELING STRATEGIES: WITH APPLICATIONS TO LINEAR MODELS, LOGISTIC AND ORDINAL REGRESSION AND SURVIVAL ANALYSIS (2ND ED.)

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REGRESSION MODELING STRATEGIES: WITH APPLICATIONS TO LINEAR MODELS, LOGISTIC AND ORDINAL REGRESSION AND SURVIVAL ANALYSIS (2ND ED.) es un libro escrito en castellano que trata sobre Ciencias, del gran escritor FRANK HARRELL , 2015 de la mano de la editorial SPRINGER INTERNATIONAL PUBLISHING, esta en formato pdf y contiene 582 paginas en español. Su numero de referencia (ISBN) es 9783319330396

  • Categoria Ciencias
  • Autor FRANK HARRELL , 2015
  • Paginas 582
  • Editorial SPRINGER INTERNATIONAL PUBLISHING
  • ISBN 9783319330396

Descripción completa del libro REGRESSION MODELING STRATEGIES: WITH APPLICATIONS TO LINEAR MODELS, LOGISTIC AND ORDINAL REGRESSION AND SURVIVAL ANALYSIS (2ND ED.) y sinopsis

This long-awaited second edition includes new chapters and sections, 225 new references and a complete R software.

As in the previous edition, this book deals with the art and science of data analysis and predictive modeling, which involves the choice and use of multiple tools.

Rather than presenting isolated techniques, this text emphasizes problem-solving strategies that address the many issues that arise when developing multivariable models using real data rather than standard textbook examples.

Regression Modelling Strategies presents large-scale case studies of non-trivial datasets rather than overly simplified illustrations of each method.

These case studies use open-access R functions that make the multiple tasks of imputation, model building, validation, and interpretation described in the book relatively easy to perform.

Most of the methods in this text apply to all regression models, but special emphasis is placed on multiple regression using generalized least squares for longitudinal data, the binary logistic model, the ordinal response models, the parametric survival regression models, and the Cox semi-parametric survival model.

As in the first edition, this text is intended for graduate students at the master's or doctoral level who have had a general introductory course in probability and statistics and who are well versed in ordinary multiple regression and intermediate algebra.

The book will also serve as a reference for data analysts and statistical methodologists, as it contains an updated survey and bibliography of modern statistical modelling techniques.

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