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Applications of regression models in epidemiology / Erick Suárez, Cynthia M. Pérez, Roberto Rivera, Melissa N. Martínez
Suárez, Erick (autor) (1953-) ; Pérez, Cynthia M. (autora) ; Rivera, Roberto (autor) ; Martínez, Melissa N. (autora) ;
Trenton, New Jersey, United States : John Wiley and Sons, Inc , 2017
Clasificación: 610.21 / A6
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010019803 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

This book is written for public health professionals and students interested in applying regression models in the field of epidemiology. The academic material is usually covered in public health courses including (i) Applied Regression Analysis, (ii) Advanced Epidemiology, and (iii) Statistical Computing. The book is composed of 13 chapters, including an introduction chapter that covers basic concepts of statistics and probability. Among the topics covered are linear regression model, polynomial regression model, weighted least squares, methods for selecting the best regression equation, and generalized linear models and their applications to different epidemiological study designs. An example is provided in each chapter that applies the theoretical aspects presented in that chapter. In addition, exercises are included and the final chapter is devoted to the solutions of these academic exercises with answers in all of the major statistical software packages, including STATA, SAS, SPSS, and R. It is assumed that readers of this book have a basic course in biostatistics, epidemiology, and introductory calculus. The book will be of interest to anyone looking to understand the statistical fundamentals to support quantitative research in public health. In addition, this book: • Is based on the authors’ course notes from 20 years teaching regression modeling in public health courses. • Provides exercises at the end of each chapter. • Contains a solutions chapter with answers in STATA, SAS, SPSS, and R. • Provides real-world public health applications of the theoretical aspects contained in the chapters. Applications of Regression Models in Epidemiology is a reference for graduate students in public health and public health practitioners.


About the Authors
1 Basic Concepts for Statistical Modeling
1.1 Introduction
1.2 Parameter Versus Statistic
1.3 Probability Definition
1.4 Conditional Probability
1.5 Concepts of Prevalence and Incidence
1.6 Random Variables
1.7 Probability Distributions
1.8 Centrality and Dispersion Parameters of a Random Variable
1.9 Independence and Dependence of Random Variables
1.10 Special Probability Distributions
1.10.1 Binomial Distribution
1.10.2 Poisson Distribution
1.10.3 Normal Distribution
1.11 Hypothesis Testing
1.12 Confidence Intervals
1.13 Clinical Significance Versus Statistical Significance
1.14 Data Management
1.14.1 Study Design
1.14.2 Data Collection
1.14.3 Data Entry
1.14.4 Data Screening
1.14.5 What to Do When Detecting a Data Issue
1.14.6 Impact of Data Issues and How to Proceed
1.15 Concept of Causality
2 Introduction to Simple Linear Regression Models
2.1 Introduction
2.2 Specific Objectives
2.3 Model Definition
2.4 lytodel Assumptions
2.5 Graphic Representation
2.6 Geometry of the Simple Regression Model
2.7 Estimation of Parameters
2.8 Variance of Estimators
2.9 Hypothesis Testing About the Slope of the Regression Line
2.9.1 Using the Student’s ṭ-Distribution
2.9.2 Using ANOVA
2.10 Coefficient of Determination R²
2.11 Pearson Correlation Coefficient
2.12 Estimation of Regression Line Values and Prediction
2.12.1 Confidence Interval for the Regression Line
2.12.2 Prediction Interval of Actual Values of the Response
2.13 Example
2.14 Predictions
2.14.1 Predictions with the Database Used by the Model
2.14.2 Predictions with Data Not Used to Create the Model
2.14.3 Residual Analysis
2.15 Conclusions
Practice Exercise
3 Matrix Representation of the Linear Regression Model
3.1 Introduction
3.2 Specific Objectives
3.3 Definition

3.3.1 Matrix
3.4 Matrix Representation of a SLRM
3.5 Matrix Arithmetic
3.5.1 Addition and Subtraction of Matrices
3.6 Matrix Multiplication
3.7 Special Matrices
3.8 Linear Dependence
3.9 Rank of a Matrix
3.10 Inverse Matrix [A-¹]
3.11 Application of an Inverse Matrix in a SLRM
3.12 Estimation of ßParameters in a SLRM
3.13 Multiple Linear Regression Model (MLRM)
3.14 Interpretation of the Coefficients in a MLRM
3.15 ANOVA in a MLRM
3.16 Using Indicator Variables (Dummy Variables)
3.17 Polynomial Regression Models
3.18 Centering
3.19 Multicollinearity
3.20 Interaction Terms
3.21 Conclusion
Practice Exercise
4 Evaluation of Partial Tests of Hypotheses in a MLRM
4.1 Introduction
4.2 Specific Objectives
4.3 Definition of Partial Hypothesis
4.4 Evaluation Process of Partial Hypotheses
4.5 Special Cases
4.6 Examples
4.7 Conclusion
Practice Exercise
5 Selection of Variables in a Multiple Linear Regression Model
5.1 Introduction
5.2 Specific Objectives
5.3 Selection of Variables According to the Study Objectives
5.4 Criteria for Selecting the Best Regression Model
5.4.1 Coefficient of Determination, R²
5.4.2 Adjusted Coefficient of Determination, R²a
5.4.3 Mean Square Error (MSE)
5.4.4 Mallows’s Cp
5.4.5 Akaike Information Criterion
5.4.6 Bayesian Information Criterion
5.4.7 All Possible Models
5.5 Stepwise Method in Regression
5.5.1 Forward Selection
5.5.2 Backward Elimination
5.5.3 Stepwise Selection
5.6 Limitations of Stepwise Methods
5.7 Conclusion
Practice Exercise
6 Correlation Analysis
6.1 Introduction
6.2 Specific Objectives
6.3 Main Correlation Coefficients Based on SLRM
6.3.1 Pearson Correlation Coefficient p
6.3.2 Relationship Between r and ß1
6.4 Major Correlation Coefficients Based on MLRM
6.4.1 Pearson Correlation Coefficient of Zero Order

6.4.2 Multiple Correlation Coefficient
6.5 Partial Correlation Coefficient
6.5.1 Partial Correlation Coefficient of the First Order
6.5.2 Partial Correlation Coefficient of the Second Order
6.5.3 Semipartial Correlation Coefficient
6.6 Significance Tests
6.7 Suggested Correlations
6.8 Example
6.9 Conclusion
Practice Exercise
7 Strategies for Assessing the Adequacy of the Linear Regression Model
7.1 Introduction
7.2 Specific Objectives
7.3 Residual Definition
7.4 Initial Exploration
7.5 Initial Considerations
7.6 Standardized Residual
7.7 Jackknife Residuals (R-Student Residuals)
7.8 Normality of the Errors
7.9 Correlation of Errors
7.10 Criteria for Detecting Outliers, Leverage, and Influential Points
7.11 Leverage Values
7.12 Cook’s Distance
7.16 Summary of the Results
7.17 Multicollinearity
7.18 Transformation of Variables
7.19 Conclusion
Practice Exercise
8 Weighted Least-Squares Linear Regression
8.1 Introduction
8.2 Specific Objectives
8.3 Regression Model with Transformation into the Original Scale of Y
8.4 Matrix Notation of the Weighted Linear Regression Model
8.5 Application of the WLS Model with Unequal Number of Subjects
8.5.1 Design without Intercept
8.5.2 Model with Intercept and Weighting Factor
8.6 Applications of the WLS Model When Variance Increases
8.6.1 First Alternative
8.6.2 Second Alternative
8.7 Conclusions
Practice Exercise
9 Generalized Linear Models
9.1 Introduction
9.2 Specific Objectives
9.3 Exponential Family of Probability Distributions
9.3.1 Binomial Distribution
9.3.2 Poisson Distribution
9.4 Exponential Family of Probability Distributions with Dispersion
9.5 Mean and Variance in EF and EDF
9.6 Definition of a Generalized Linear Model
9.7 Estimation Methods

9.8 Deviance Calculation
9.9 Hypothesis Evaluation
9.10 Analysis of Residuals
9.11 Model Selection
9.12 Bayesian Models
9.13 Conclusions
10 Poisson Regression Models for Cohort Studies
10.1 Introduction
10.2 Specific Objectives
10.3 Incidence Measures
10.3.1 Incidence Density
10.3.2 Cumulative Incidence
10.4 Confounding Variable
10.5 Stratified Analysis
10.6 Poisson Regression Model
10.7 Definition of Adjusted Relative Risk
10.8 Interaction Assessment
10.9 Relative Risk Estimation
10.10 Implementation of the Poisson Regression Model
10.11 Conclusion
Practice Exercise
11 Logistic Regression in Case-Control Studies
11.1 Introduction
11.2 Specific Objectives
11.3 Graphical Representation
11.4 Definition of the Odds Ratio
11.5 Confounding Assessment
11.6 Effect Modification
11.7 Stratified Analysis
11.8 Unconditional Logistic Regression Model
11.9 Types of Logistic Regression Models
11.9.1 Binary Case
11.9.2 Binomial Case
11.10 Computing the ORcrude
11.11 Computing the Adjusted OR
11.12 Inference on OR
11.13 Example of the Application of ULR Model: Binomial Case
11.14 Conditional Logistic Regression Model
11.15 Conclusions
Practice Exercise
12 Regression Models in a Cross-Sectional Study
12.1 Introduction
12.2 Specific Objectives
12.3 Prevalence Estimation Using the Normal Approach
12.4 Definition of the Magnitude of the Association
12.5 POR Estimation
12.5.1 Woolf's Method
12.5.2 Exact Method
12.6 Prevalence Ratio
12.7 Stratified Analysis
12.8 Logistic Regression Model
12.8.1 Modeling Prevalence Odds Ratio
12.8.2 Modeling Prevalence Ratio
12.9 Conclusions
Practice Exercise

13 Solutions to Practice Exercises
Chapter 2 Practice Exercise
Chapter 3 Practice Exercise
Chapter 4 Practice Exercise
Chapter 5 Practice Exercise
Chapter 6 Practice Exercise
Chapter 7 Practice Exercise
Chapter 8 Practice Exercise
Chapter 10 Practice Exercise
Chapter 11 Practice Exercise
Chapter 12 Practice Exercise

Regression models for categorical dependent variables using stata / J. Scott Long, Jeremy Freese
Long, J. Scott ; Freese, Jeremy (coaut.) ;
College Station, Texas : Stata Press Publication :: StataCorp LP , 2014
Clasificación: 519.536 / L6
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010018492 (Disponible)
Disponibles para prestamo: 1
Resumen en: Inglés |
Resumen en inglés

Regression Models for Categorical Dependent Variables Using Stata, Third Edition, by J. Scott Long and Jeremy Freese, is an essential reference for those who use Stata to fit and interpret regression models for categorical data. Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text decisively fills the void. The third edition is divided into two parts. Part I begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fitting, and interpretation of models for categorical dependent variables. The book is thus accessible to new users of Stata and those who are new to categorical data analysis. Part II is devoted to a comprehensive treatment of estimation and interpretation for binary, ordinal, nominal, and count outcomes. Readers familiar with previous editions will find many changes in the third edition. An entire chapter is now devoted to interpretation of regression models using predictions. This concept is explored in greater depth in Part II. The authors also discuss how many improvements made to Stata in recent years—factor variables, marginal effects with margins, plotting predictions using marginsplot—facilitate analysis of categorical data. The authors advocate a variety of new methods that use predictions to interpret the effect of variables in regression models. Readers will find all discussion of statistical concepts firmly grounded in concrete examples. All the examples, datasets, and author-written commands are available on the authors' website, so readers can easily replicate the examples with Stata.

Examples in the new edition also illustrate changes to the authors' popular SPost commands after a recent rewrite inspired by the authors' evolving views on interpretation. Readers will note that SPost now takes full advantage of the power of the margins command and the flexibility of factor-variable notation. Long and Freese also provide a suite of new commands, including mchange, mtable, and mgen. These commands complement margins, aiding model interpretation, hypothesis testing, and model diagnostics. They offer the same syntactical convenience features that users of Stata expect, for example including powers or interactions of covariates in regression models and seamlessly working with complex survey data. The authors also discuss how to use these commands to estimate marginal effects, either averaged over the sample or evaluated at fixed values of the regressors. The third edition of Regression Models for Categorical Dependent Variables Using Stata continues to provide the same high-quality, practical tutorials of previous editions. It also offers significant improvements over previous editions—new content, updated information about Stata, and updates to the authors' own commands. This book should be on the bookshelf of every applied researcher analyzing categorical data and is an invaluable learning resource for students and others who are new to categorical data analysis.

- Libro sin arbitraje
Introducción a los modelos de regresión / Lucio A. Pat Fernández, Aarón H. Martínez Menchaca, Juan M. Pat Fernández, David Martínez Luis
Pat Fernández, Lucio Alberto ; Martínez Menchaca, Aarón H. (coaut.) ; Pat Fernández, Juan Manuel (coaut.) ; Martínez Luis, David (coaut.) ;
Ciudad del Carmen, Campeche, México : Universidad Autónoma del Carmen :: Plaza y Valdés , 2013
Clasificación: EE/519.536 / I5
Bibliotecas: Campeche
SIBE Campeche
ECO040005252 (Disponible)
Disponibles para prestamo: 1

Quantile regression for spatial data / Daniel P. McMillen
McMillen, Daniel P. ;
Berlin : Springer , c2013
Clasificación: F/519.536 / M3
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010007647 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

"Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an explanatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail."


1 Quantile Regression: An Overview
1.1 A Monte Carlo Study of Gentrification
1.2 Quantile Regression Estimates
1.3 Implied Distribution of Sales Prices
1.4 Nonlinear Quantile Regression
1.5 Conclusion
2 Linear and Nonparametric Quantile Regression
2.1 Linear Quantile Regression: Simulated Data
2.2 Simulating the Distribution of the Dependent Variable
2.3 The Effect of a Discrete Change in an Explanatory Variable
2.4 Nonparametric Quantile Regression
2.5 Conclusion
3 A Quantile Regression Analysis of Assessment Regressivity
3.1 A Monte Carlo Analysis of Assessment Ratios
3.2 Assessment Ratios in DuPage County, Illinois
3.3 Conclusion
4 Quantile Version of the Spatial AR Model
4.1 Quantile Regression with an Endogenous Explanatory Variable
4.2 An Application to Hedonic House Price Functions
4.3 Conclusion
5 Conditionally Parametric Quantile Regression
5.1 CPAR Quantile Regression for Spatial Data
5.2 An Empirical Example: House Prices in Tacoma, WA
5.3 Assessment Ratios in Cook County, IL
5.4 Conclusion
6 Guide to Further Reading

Análisis de datos con Stata / Modesto Escobar Mercado, Enrique Fernández Macías, Fabrizio Bernardi
Escobar Mercado, Modesto ; Fernández Macías, Enrique (coaut.) ; Bernardi, Fabrizio (coaut.) ;
Madrid, España : Centro de Investigaciones Sociológicas , 2012
Clasificación: 005.55 / E8
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010018189 (Disponible) , ECO010017575 (Disponible) , ECO010006395 (Disponible)
Disponibles para prestamo: 3
Índice | Resumen en: Español | Inglés |
Resumen en español

Análisis de datos con Stata, por Escobar, Fernández, y Bernardi, es un excelente recurso para usuarios de Stata con niveles de principiante o intermedio y que desean familiarizarse rápidamente con las facilidades que ofrece el software para el manejo de los datos y el análisis estadístico. Los autores ilustran el uso de Stata para estadística descriptiva y análisis de regresión, usando ejemplos que están principalmente enfocados en investigaciones asociadas a las ciencias sociales, pero que son fáciles de seguir por usuarios en diferentes áreas de trabajo. El libro contiene cuatro capítulos (1–3, y 5) que están explicitamente dirigidos a la descripción de las herramientas de Stata relacionadas con el almacenamiento, la creación, y el manejo de archivos, así como también con la ejecución de operaciones comunes en el manejo de los datos. El capítulo 6 ofrece una buena presentación de los comandos y opciones para producir gráficos en Stata. Se incluyen una variedad de ejemplos que permiten al usuario comenzar con la sintaxis simple para gráficos unidimensionales, y luego continuar con comandos y opciones mas complejas para gráficos que requieren mayor elaboración (también se incluye una sección acerca del editor de gráficos). Los capítulos 4, 7, y 8 se concentran en el uso de Stata para estadística descriptiva e inferencia estadística. En los capítulos 9 y 10, los autores ilustran algunas de las herramientas disponibles para ajustar modelos lineales e implementar tests de diagnóstico para las correspondientes regresiones. Se muestran las líneas de comando y las tablas de salida de un conjunto de ecuaciones para una variable dependiente asociada a la mortalidad infantil, en función de un par de variables macroeconómicas y un grupo de variables artificiales que representan diferentes regiones geográficas.

Estas últimas variables son introducidas a través de la funcionalidad añadida en Stata 11 para el manejo de variables factoriales. El libro continúa con un par de capítulos acerca de regresiones logísticas y multinomiales. Luego de ajustar algunos modelos asociados a posturas políticas individuales explicadas por características demográficas, se utiliza una combinación de comandos oficiales y comandos escritos por usuarios para calcular predicciones y estadísticos de diagnóstico para la regresión. Los últimos dos capítulos usan ejemplos asociados al mercado laboral para mostrar algunos de los comandos de Stata que pueden ser utilizados para el análisis de la historia de acontecimientos, y para el análisis de datos de encuesta. Los autores lograron combinar un material teórico didáctico sobre estadística descriptiva y análisis de regresión con una adecuada introducción al manejo de Stata. Por lo cual, este libro representa una herramienta sobresaliente para aquellos que están comenzando a trabajar con Stata o incluso para los que tienen un nivel básico intermedio de conocimiento del software. La primera edición del libro se agotó y los autores decidieron tomar esta oportunidad para actualizar las salidas del software y los cambios de la interfaz de trabajo que experimentaron alguna modificación debido a las adiciones y mejoras incorporadas en la versión 12 de Stata. Adicionalmente, el libro muestra algunos cambios en el manejo de los datos que fueron incorporados en Stata 12. Particularmente, se ilustran las modificaciones en la sintaxis del comando merge, y la nueva instrucción (import excel) que permite importar los datos directamente con el formato de Excel.

Resumen en inglés

Análisis de datos con Stata, by Escobar, Fernández, and Bernardi, is an excellent resource for new and intermediate Stata users who would like to quickly become familiar with data-management facilities that help prepare data for statistical analysis. The authors illustrate the use of Stata for descriptive statistics and regression analysis with examples that are mainly focused on social-science research but that are easy to follow for users with different backgrounds. There are four chapters (1–3 and 5) explicitly devoted to the description of Stata tools related to loading, creating, and handling files, as well as to performing common data-management tasks. Chapter 6 provides a good presentation of a series of commands and options to produce graphs in Stata. It includes a variety of examples that allow the user to start with the simple syntax for unidimensional graphs and continue with more complex commands and options that can be used for more elaborate graphs. It also includes a section on the Graph Editor. Chapters 4, 7, and 8 concentrate on the use of Stata for descriptive statistics and basic statistical inference. In chapters 9 and 10, the authors illustrate some of the model fit and regression diagnostic tools available for linear models. They show the command lines and output for a few different equation specifications for child mortality regressed on a couple of macroeconomic variables and a few geographical dummy variables. The latter are introduced by using the factor-variables facilities added in Stata 11.

The book continues with a couple of chapters on logistic and multinomial logistic regression. After fitting models associated to individual political stance explained by certain demographic characteristics, a combination of official and user-written commands is used to compute predictions and regression diagnostic statistics. The last two chapters use examples linked to employment to show some of the Stata commands that implement event and survey analysis. The authors were able to combine a fairly complete introduction to Stata with theoretical readings on statistical analysis, which makes this book an outstanding tool for those who are starting their statistical analysis journey with Stata or even for those with a basic-intermediate knowledge about the software. The first edition was sold out, so the authors decided to update the outputs and the elements of the graphical user interface that were affected by the additions and improvements in Stata 12. The book shows some of the changes in data management–related commands in version 12. In particular, the authors illustrate the modifications in the merge command and also the new instruction (import excel) that allows the importing of spreadsheets in Excel format.


1. Introducción
2. Primeros Pasos con Stata
2.1 La información en los archivos de Stata
2.2 La interfaz de Stata
2.3 Las ventanas de Stata
2.4 Modos de trabajo en Stata
2.5 El fichero de resultados
2.6 Las variables de la matriz de datos
2.7 Ejercicios
3. Introducción de Datos
3.1 Introducción manual de datos
3.2 Lectura de datos con Stata
3.3 Fusión de ficheros
3.4 Ejercicios
4. Estadísticas de una Sola Variable
4.1 Clasificación de variables
4.2 La tabla de distribución de frecuencias
4.3 Estadísticos resúmenes de distribuciones
4.4 Obtención de las medidas características de una distribución
4.5 La ponderación de los datos
4.6 El error típico
4.7 Ejercicios
5. Manipulación y Modificación de Datos
5.1 Manipulación de datos
5.2 Generación y modificación de variables
5.3 Características e instrucciones especiales
5.4 Ejercicios
6. Gráficos con Stata
6.1 Características de los gráficos de Stata
6.2 Gráficos unidimensionales
6.3 Gráficos bidimensionales
6.4 Componentes de los gráficos
6.5 Esquemas
6.6 El editor de gráficos
6.7 Ejercicios
7. La Prueba Estadística y las Comparaciones
7.1 Pruebas de una sola variable
7.2 Comparación de dos variables
7.3 Comparaciones de dos muestras (independientes)
7.4 Comparaciones de k muestras independientes
7.5 Comparaciones de k muestras dependientes
7.6 Ejercicios
8. Confección y Análisis de Tablas Con Stata
8.1 Tablas de contingencia de dos variables
8.2 Más de dos variables
8.3 Otras tablas especiales
8.4 Las tablas de respuesta múltiple
7.5 Ejercicios
9. La Regresión
9.1 Nube de puntos, varianza y correlación entre dos variables
9.2 La regresión simple
9.3 Bondad de ajuste de la regresión
9.4 Inferencias en la regresión simple
9.5 Regresión múltiple
9.6 Regresión con variables ficticias
9.7 Regresiones con interacción

9.8 Otras relaciones funcionales de la regresión
9.9 Ejercicios
10. Diagnóstico de la Regresión
10.1 Supuestos de la regresión lineal
10.2 Análisis de los casos en la regresión
10.3 Regresiones especiales
10.4 Regresión robusta
10.5 Regresión de cuantiles
10.6 Regresión por bandas
10.7 Ejercicios
11. La Regresión Logística
11.1 El modelo estadístico
11.2 Estimación del modelo
11.3 Diagnóstico del modelo
11.4 Comparación de modelos
11.5 Interpretación del modelo
11.6 Ejercicios
12. Regresión Logística Variable Ordinal y Multinomial
12.1 El modelo estadístico del logit ordinal
12.2 Estimación e interpretación del modelo
12.3 El supuesto de regresiones paralelas o razones proporcionales
12.4 Regresión logística para variable dependiente nominal
12.5 Estimación e interpretación del modelo
12.6 El supuesto de independencia de alternativas irrelevantes
12.7 Ejercicios
13. El Análisis de la Historia de Acontecimientos con Stata
13.1 Qué es y cómo funciona el AHA
13.2 El AHA con Stata: instrucciones para definir los datos
13.3 La función de supervivencia
13.4 Modelos de la tasa de transición con tiempo continuo
13.5 Ejercicios
14. Análisis de Datos de Encuesta con Stata
14.1 Ajustes en el análisis de muestras complejas
14.2 Ponderaciones, estratos y conglomerados
14.3 Un ejemplo práctico con Stata. Las órdenes svy
14.4 Ejercicios
15. Bibliografía Comentada
16. Índice de Instrucciones
17. Índice de Cuadros
18. Índice de Ilustraciones
19. Índice de Gráficos

A beginner's guide to generalized additive models with R / Alain F. Zuur
Zuur, Alain F. ;
Newburgt, United Kingdom : Highland Statistics Ltd. , 2012
Clasificación: 519.50285 / Z9
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010014814 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

A Beginner’s Guide to Generalized Additive Models with R is, as the title implies, a practical handbook for the non-statistician. The author’s philosophy is that the shortest path to comprehension of a statistical technique without delving into extensive mathematical detail is through programming its basic principles in, for example, R. Beginner's Guide to GAM Not a series of cookbook exercises, the author uses data from biological studies to go beyond theory and immerse the reader in real-world analysis with its inherent untidiness and challenges. The book begins with a review of multiple linear regression using research on human crania size and ambient light levels and continues with an introduction to additive models based on deep sea fishery data. Research on pelagic bioluminescent organisms demonstrates simple linear regression techniques to program a smoother. In Chapter 4 the deep sea fishery study is revisited for a discussion of generalized additive models. The remaining chapters present detailed case studies illustrating the application of Gaussian, Poisson, negative binomial, zero-inflated Poisson, and binomial generalized additive models using seabird, squid, and fish parasite studies.


Datasets used in this book
Cover art
1 Review of multiple linear regression
1.1 Light levels and size of the human visual system
1.2 The variables
1.3 A protocol for the analysis
1.4 Data exploration
1.5 Multiple linear regression
1.5.1 Underlying statistical theory
1.5.2 Multiple linear regression
1.5.3 Fitting the model in R and estimated parameters
1.6 Finding the optimal model
1.7 Degrees of freedom
1.8 Model validation
1.9. Model interpretation
1.10 What happens if we use collinear covariates?
1.11 Should we have applied a mixed effects model?
1.12 What to do if things go wrong
1.13 What to present in a paper
2 Introduction to additive models using deep-sea fisheries data
2.1 Impact of deep-sea fisheries
2.2 First encounter with smoothers
2.2.1 Applying linear regression
2.2.2 Applying cubic polynomials
2.2.3 A simple GAM
2.2.4 Moving average and LOESS smoothers
2.3 Applying GAM in R using the mgcv package
2.4 Cross-validation
2.5 Model validation
2.5.1 Normality and homogeneity
2.5.2 Independence
2.6 Extending the GAM with more covariates
2.6.1 GAM with a smoother and a nominal covariate
2.6.2 GAM with an interaction term; first implementation
2.6.3 GAM with an interaction term; second implementation
2.6.4 GAM with an interaction term; third implementation
2.7 Transforming the density data
2.8 Allowing for heterogeneity
2.9 Transforming and allowing for heterogeneity
2.10 What to present in a paper
3 Technical aspects of GAM using pelagic bioluminescent organisms
3.1 Pelagic bioluminescent organism data
3.2 Linear regression
3.3 Polynomial regression model
3.4. Linear spline regression
3.5 Quadratic spline regression
3.6 Cubic regression splines
3.7 The number of knots
3.8 Penalized quadratic spline regression
3.9 Other smoothers
3.10 Cubic smoothing spline

3.11 Summary of smoother types
3.12 Degrees of freedom of a smoother*
3.13 Bias-variance trade-off
3.14 Confidence intervals
3.15 Using the function gam in mgcv
3.16 The danger of using GAM
3.17 Additive models with multiple smoothers
4 Introducing generalized additive models using deep-sea fishery data
4.1 From additive models to generalized additive models
4.2 Review of GLM
4.2.1 Distribution
4.2.2 Predictor function
4.2.3 Link function
4.3 Start with GLM or GAM?
4.4 Results of Poisson and negative binomial GLM
4.5 Using the offset in a GLM or GAM
4.6 Poisson GLM with offset
4.7 Negative binomial GLM with offset
4.8 Poisson and negative binomial GAM with offset
4.9 What to present in paper
5 Additive modelling applied on stable isotope ratios of ocean squid
5.1 Stable isotope ratios of squid
5.2 The variables
5.3 Data exploration
5.4 Brainstorming
5.5 Applying the multiple linear regression model
5.6 Applying an additive model
5.7 Testing linearity versus non-linearity
5.7.1 Programming a smoother manually**
5.7.2 Summary of the mathematics
5.8 Consequences of ignoring collinearity in the additive model
5.9 Discussion
5.10 What to present in a paper
6 Generalized Additive Models applied on northern gannets
6.1 Northern gannets in the North Sea
6.2 The variables
6.3 Brainstorming
6.4 Data exploration
6.5 Building up the complexity of the GAMs
6.6 Zero-inflated GAM
6.6.1 A zero-inflated model for the gannet data
6.6.2 ZIP GAM using gamlss
6.7 Discussion
6.8 What to present in a paper
7 Generalized Additive Models applied on parasites of Argentine hake
7.1 Parasites of Argentine hake in the Argentine Sea
7.2 The variables
7.3 Data exploration
7.4 Brainstorming
7.5 Applying binomial GAM
7.6 Discussion
7.7 What to present in a paper

Introducción a la econometría / James H. Stock, Mark W. Watson ; traducción de María Arrazola Vacas y Leticia Rodas Alfaya
Stock, James H. ; Watson, Mark W. (coaut.) ; Arrazola Vacas, María (tr.) ; Rodas Alfaya, Leticia (cotr.) ;
Madrid, España : Pearson Educación , 2012
Clasificación: 330.1543 / S8
Bibliotecas: Chetumal
SIBE Chetumal
ECO030008008 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Español |
Resumen en español

La Econometría puede ser una asignatura entretenida tanto para el profesor como para el estudiante. La realidad de la economía, los negocios y el Estado es un lugar complicado y confuso, repleto de ideas contrapuestas y preguntas que necesitan respuestas. Esta rama de la ciencia económica abre una ventana en nuestro complicado mundo que permite ver las relaciones sobre las cuales las personas, las empresas y los gobiernos basan sus decisiones. Este texto está diseñado para adaptarse a un primer curso de Econometría. Para esto hemos creado un curso de introducción con aplicaciones interesantes que motiven la teoría y además la hagan coincidir con las aplicaciones. Esta premisa representa una clara diferencia con la mayor parte de los libros de econometría en los que los modelos teóricos y los supuestos no coinciden con las aplicaciones. No es de extrañar que algunos estudiantes cuestionen la importancia de la econometría después de pasar mucho tiempo dedicado a resolver las cuestiones concretas cuando no ven la relación con la realidad. Creemos que con este libro el estudiante verá inmediatamente la aplicación directa, utilizando las herramientas econométricas y su utilización práctica. También encontramos en el libro, aplicaciones de la econometría a las realidades económicas actuales.


Contenido abreviado
Parte I Introducción y repaso
Capítulo 1 Cuestiones económicas y datos
Capítulo 2 Repaso de probabilidad
Capítulo 3 Repaso de estadística
Parte II Los fundamentos del análisis de regresión
Capítulo 4 Regresión lineal con regresor único
Capítulo 5 Regresión con regresor único: contrastes de hipótesis e intervalos de confianza
Capítulo 6 Regresión lineal con varios regresores
Capítulo 7 Contrastes de hipótesis e intervalos de confianza en regresión múltiple
Capítulo 8 Funciones de regresión no lineales
Capítulo 9 Evaluación de estudios basados en regresión múltiple
Parte III Otros temas relacionados con el análisis de regresión
Capítulo 10 Regresión con datos de panel
Capítulo 11 Regresión con variable dependiente binaria
Capítulo 12 Regresión con variables instrumentales
Capítulo 13 Experimentos y cuasi experimentos
Parte IV Análisis de regresión con datos de series temporales económicas
Capítulo 14 Introducción a la regresión de series temporales y predicción
Capítulo 15 Estimación de efectos causales dinámicos
Capítulo 16 Otros temas relacionados con la regresión en series temporales
Parte V Teoría econométrica del análisis de regresión
Capítulo 17 Teoría de regresión lineal con regresor único
Capítulo 18 Teoría de regresión múltiple

Estimation of tropical forest structure from SPOT-5 satellite images
Castillo Santiago, Miguel Ángel ; Ricker, Martin (coaut.) ; De Jong, Bernardus Hendricus Jozeph (coaut.) ;
Contenido en: Journal International Journal of Remote Sensing Vol. 31, no. 10 (March 2010), p. 2767-2782 ISSN: 0143-1161
Resumen en: Inglés |
Resumen en inglés

Predictions of tropical forest structure at the landscape level still present relatively high levels of uncertainty. In this study we explore the capabilities of high-resolution Satellite Pour l'Observation de la Terre (SPOT)-5 XS images to estimate basal area, tree volume and tree biomass of a tropical rainforest region in Chiapas, Mexico. SPOT-5 satellite images and forest inventory data from 87 sites were used to establish a multiple linear regression model. The 87 0.1-ha plots covered a wide range of forest structures, including mature forest, with values from 74.7 to 607.1 t ha-1. Spectral bands, image transformations and texture variables were explored as independent variables of a multiple linear regression model. The R2s of the final models were 0.58 for basal area, 0.70 for canopy height, 0.73 for bole volume, and 0.71 for biomass. A leave-one-out cross-validation produced a root mean square. error (RMSE) of 5.02 m2 ha-1 (relative RMSE of 22.8%) for basal area; 3.22 m (16.1%) for canopy height; 69.08 m3 ha-1 (30.7%) for timber volume, and 59.3 t ha-1 (21.2%) for biomass. In particular, the texture variable 'variance of near-infrared' turned out to be an excellent predictor for forest structure variables.

- Artículo con arbitraje
*En hemeroteca, SIBE-San Cristóbal
Assessing species density and abundance of tropical trees from remotely sensed data and geostatistics
Hernández Stefanoni, José Luis ; Dupuy, Juan Manuel (coaut.) ; Castillo Santiago, Miguel Ángel (coaut.) ;
Contenido en: Applied Vegetation Science Vol. 12, no. 4 (October 2009), p. 398-414 ISSN: 1402-2001
Bibliotecas: San Cristóbal
SIBE San Cristóbal
48114-10 (Disponible)
Disponibles para prestamo: 1
Nota: En hemeroteca, SIBE-San Cristóbal
Resumen en: Inglés |
Resumen en inglés

Methods: Spatial prediction of species density and abundance of species for three functional groups was performed using regression kriging, which considers the linear relationship between dependent and explanatory variables, as well as the spatial dependence of the observations. These relationships were explored using regression analysis with species density and abundance of species of three functional groups as dependent variables, and reflectance values of spectral bands, computed NDVI (normalized difference vegetation index), standard deviation of NDVI and texture measurements of Landsat 7 Thematic Mapper (TM) imagery as explanatory variables. Akaike information criterion was employed to select a set of candidate models and calculate model-averaged parameters. Variogram analysis was used to analyze the spatial structure of the residuals of the linear regressions. Results: Species density of trees was related to reflectance values of TM4, NDVI and spatial heterogeneity of land cover types, while the abundance of species in functional groups showed different patterns of association with remotely sensed data. Models that accounted for spatial autocorrelation improved the accuracy of estimates in all cases. Conclusions: Our approach can substantially increase the accuracy of the spatial estimates of species richness and abundance of tropical tree species and can help guide and evaluate tropical forest management and conservation.

Logistic regression models / Joseph M. Hilbe
Hilbe, Joseph M. (1944-) ;
Boca Raton, Florida, United States : CRC Press , c2009
Clasificación: 519.536 / H5
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010017654 (Disponible) , ECO010009012 (Disponible)
Disponibles para prestamo: 2
Índice | Resumen en: Inglés |
Resumen en inglés

Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. Examples illustrate successful modeling The text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties of overdispersion, and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text.


Chapter 1 Introduction
1.1 The Normal Model
1.2 Foundation of the Binomial Model
1.3 Historical and Software Considerations
1.4 Chapter Profiles
Chapter 2 Concepts Related to the Logistic Model
2.1 2×2 Table Logistic Model
2.2 2×k Table Logistic Model
2.3 Modeling a Quantitative Predictor
2.4 Logistic Modeling Designs

Chapter 14 Other Types of Logistic-Based Models
14.1 Survey Logistic Models
14.1.1 Interpretation
14.2 Scobit-Skewed Logistic Regression
14.3 Discriminant Analysis
14.3.1 Dichotomous Discriminant Analysis
14.3.2 Canonical Linear Discriminant Analysis
14.3.3 Linear Logistic Discriminant Analysis
Chapter 15 Exact Logistic Regression
15.1 Exact Methods
15.2 Alternative Modeling Methods
15.2.1 Monte Carlo Sampling Methods
15.2.2 Median Unbiased Estimation
15.2.3 Penalized Logistic Regression
Appendix A: Brief Guide to Using Stata Commands
Appendix B: Stata and R Logistic Models
Appendix C: Greek Letters and Major Functions
Appendix D: Stata Binary Logistic Command
Appendix E: Derivation of the Beta-Binomial
Appendix F: Likelihood Function of the Adaptive Gauss–Hermite Quadrature Method of Estimation
Appendix G: Data Sets
Appendix H: Marginal Effects and Discrete Change
Author Index
Subject Index