Detalles MARC
| 000 -LEADER |
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| campo de control de longitud fija |
08721nam a2200373 a 4500 |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN |
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| campo de control |
20260701202235.0 |
| 006 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA--CARACTERÍSTICAS DEL MATERIAL ADICIONAL |
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| campo de control de longitud fija |
m d |
| 007 - CAMPO FIJO DE DESCRIPCIÓN FÍSICA--INFORMACIÓN GENERAL |
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| campo de control de longitud fija |
cr cnu---uuuuu |
| 008 - DATOS DE LONGITUD FIJA--INFORMACIÓN GENERAL |
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| campo de control de longitud fija |
150814s2015 s 000 0 eng d |
| 020 ## - NÚMERO INTERNACIONAL ESTÁNDAR DEL LIBRO |
|---|
| Número Internacional Estándar del Libro |
9783319194257 |
| -- |
978-3-319-19425-7 |
| 040 ## - FUENTE DE CATALOGACIÓN |
|---|
| Centro catalogador/agencia de origen |
CO-CtgCURN |
| Lengua de catalogación |
spa |
| Centro/agencia transcriptor |
coctgcurn |
| 082 04 - NÚMERO DE LA CLASIFICACIÓN DECIMAL DEWEY |
|---|
| Número de clasificación |
519.5 |
| Información de edición |
23 |
| 100 1# - ENTRADA PRINCIPAL--NOMBRE DE PERSONA |
|---|
| Nombre de persona |
Harrell, Jr., Frank E., |
| Término indicativo de función/relación |
autor. |
| 245 10 - MENCIÓN DEL TÍTULO |
|---|
| Título |
Regression Modeling Strategies |
| Medio |
[electronic resource] : |
| Resto del título |
With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis / |
| Mención de responsabilidad, etc. |
by Frank E. Harrell , Jr. |
| 250 ## - MENCION DE EDICION |
|---|
| Mención de edición |
Segunda edicion. |
| 260 #4 - PUBLICACIÓN, DISTRIBUCIÓN, ETC. |
|---|
| Lugar de publicación, distribución, etc. |
Cham : : : |
| Nombre del editor, distribuidor, etc. |
Springer International Publishing : : : |
| -- |
Imprint: Springer,,, |
| Fecha de publicación, distribución, etc. |
2015. |
| 260 #1 - PUBLICACIÓN, DISTRIBUCIÓN, ETC. |
|---|
| Fecha de publicación, distribución, etc. |
2015. |
| 300 ## - DESCRIPCIÓN FÍSICA |
|---|
| Extensión |
XXV, 582 p. 157 illus., 53 illus. in color. : |
| Otros detalles físicos |
online resource. |
| 336 ## - TIPO DE CONTENIDO |
|---|
| Término de tipo de contenido |
texto |
| Código de tipo de contenido |
txt |
| Fuente |
rdacontent |
| 337 ## - TIPO DE MEDIO |
|---|
| Nombre/término del tipo de medio |
computador |
| Código del tipo de medio |
c |
| Fuente |
rdamedia |
| 338 ## - TIPO DE SOPORTE |
|---|
| Nombre/término del tipo de soporte |
recurso en línea |
| Código del tipo de soporte |
cr |
| Fuente |
rdacarrier |
| 490 1# - MENCIÓN DE SERIE |
|---|
| Mención de serie |
Springer Series in Statistics, |
| Número Internacional Normalizado para Publicaciones Seriadas |
0172-7397 |
| 504 ## - NOTA DE BIBLIOGRAFÍA, ETC. |
|---|
| Nota de bibliografía, etc. Nota |
Incluye referencias bibliográficas e índice. |
| 505 0# - NOTA DE CONTENIDO CON FORMATO |
|---|
| Nota de contenido con formato |
Introduction -- General Aspects of Fitting Regression Models -- Missing Data -- Multivariable Modeling Strategies -- Describing, Resampling, Validating and Simplifying the Model -- R Software -- Modeling Longitudinal Responses using Generalized Least Squares -- Case Study in Data Reduction -- Overview of Maximum Likelihood Estimation -- Binary Logistic Regression -- Binary Logistic Regression Case Study 1 -- Logistic Model Case Study 2: Survival of Titanic Passengers -- Ordinal Logistic Regression -- Case Study in Ordinal Regression, Data Reduction and Penalization.-℗ Regression Models for Continuous Y and Case Study in Ordinal Regression -- Transform-Both-Sides Regression -- Introduction to Survival Analysis -- Parametric Survival Models -- Case Study in Parametric Survival Modeling and Model Approximation -- Cox Proportional Hazards Regression Model -- Case Study in Cox Regression -- Appendix.℗ ℗ ℗. |
| 520 ## - RESUMEN, ETC. |
|---|
| Sumario, etc. |
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. As in the first edition, this text is intended for Masters' or Ph.D. level graduate students who have had a general introductory probability and statistics course 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 up-to-date survey and bibliography of modern statistical modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering, and marketing. |
| 533 ## - NOTA DE REPRODUCCIÓN |
|---|
| Tipo de reproducción |
Electronic resource. |
| Lugar de la reproducción |
Dordrecht : |
| Institución responsable de la reproducción |
Springer Netherlands, |
| Fecha de la reproducción |
2015. |
| 650 #0 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Estadística. |
| 650 #0 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Estadística matemática. |
| 852 ## - LOCALIZACIÓN |
|---|
| Ubicación |
MHC |
| Sublocalización o colección |
MHC |
| Ubicación en estantería |
CF |
| Parte de clasificación |
519.5 |
| Parte de ítem |
H296 |
| 856 7# - LOCALIZACIÓN Y ACCESO ELECTRÓNICOS |
|---|
| Identificador Uniforme de Recurso |
<a href="https://unicurn.sharepoint.com/:b:/s/biblioteca/EYYeFZMgchVDhPimz_2KJ3wB4NbZH0RTqwJmQCw80tpTHw?e=5tkc1i">https://unicurn.sharepoint.com/:b:/s/biblioteca/EYYeFZMgchVDhPimz_2KJ3wB4NbZH0RTqwJmQCw80tpTHw?e=5tkc1i</a> |
| Nota pública |
<img src="/screens/gifs/go4.gif" alt="Go button" border="0" width="21" height="21" hspace="7" align=middle"> Vea este libro electrónico |
| 917 77 - |
|---|
| -- |
Introduction -- General Aspects of Fitting Regression Models -- Missing Data -- Multivariable Modeling Strategies -- Describing, Resampling, Validating and Simplifying the Model -- R Software -- Modeling Longitudinal Responses using Generalized Least Squares -- Case Study in Data Reduction -- Overview of Maximum Likelihood Estimation -- Binary Logistic Regression -- Binary Logistic Regression Case Study 1 -- Logistic Model Case Study 2: Survival of Titanic Passengers -- Ordinal Logistic Regression -- Case Study in Ordinal Regression, Data Reduction and Penalization.- Regression Models for Continuous Y and Case Study in Ordinal Regression -- Transform-Both-Sides Regression -- Introduction to Survival Analysis -- Parametric Survival Models -- Case Study in Parametric Survival Modeling and Model Approximation -- Cox Proportional Hazards Regression Model -- Case Study in Cox Regression -- Appendix |
| 917 99 - |
|---|
| -- |
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. As in the first edition, this text is intended for Masters' or Ph. D. level graduate students who have had a general introductory probability and statistics course 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 up-to-date survey and bibliography of modern statistical modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering, and marketing |
| 942 ## - ELEMENTOS DE ENTRADA SECUNDARIOS (KOHA) |
|---|
| Tipo de ítem Koha |
Libros electrónicos |
| Parte de clasificación |
519.5 |
| Parte de ítem |
H296 |
| Fuente de la clasificación o esquema de estantería |
Clasificación Decimal Dewey |
