Libros de estadística y probabilidad, página 6 de 115

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the books R data package gamair, to enable use as a course text or for self-study.

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 modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets 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 generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric 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 PhD. 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 modelling techniques.

This book contains the ideas of functional data analysis by a number of case studies. The case studies are accessible to research workers in a wide range of disciplines. Every reader should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. There is an associated web site with MATLABr and S?PLUSr implementations of the methods discussed.

This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website with code, data and additional materials.

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio--temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations

This is the second edition of a highly succesful book which has sold nearly 3000 copies world wide since its publication in 1997. Many chapters will be rewritten and expanded due to a lot of progress in these areas since the publication of the first edition. Bernard Silverman is the author of two other books, each of which has lifetime sales of more than 4000 copies. He has a great reputation both as a researcher and an author. This is likely to be the bestselling book in the Springer Series in Statistics for a couple of years.

Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. * Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation * Brings together experts from a broad range of fields * Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species * Helps you choose the right software * Draws on numerous examples based on real biological datasets----------------------El metanalisis es una potente herramienta estadistica que permite efectuar investigaciones mediante el uso de estudios realizados de forma independiente. Este se trata del primer manual sobre metanalisis escrito especificamente para ecologos y biologos evolutivos, proporcionando una introduccion de gran valor para los que deseen iniciarse en este metodo. Los capitulos han sido elaborados por expertos diversos, que conducen al lector a traves de las diferentes etapas, desde la formulacion de problemas hasta la presentacion de resultados. Explican tanto las ventajas del enfoque metanalitico como los puntos flacos de esta aproximacion. Describen diferentes vias de abordar este tipo de analisis, basando las explicaciones en casos practicos reales.

Este "Diccionario" es el resultado de varios años de trabajo y práctica en el campo de la estadística, aplicada especialmente a la educación ya los aspectos psicosociológicos. Con su exposición clara y concreta, mediante los ejemplos que ilustran los conceptos fundamentales, constituye un instrumento de trabajo imprescindible para los que se inician en el estudio de esta ciencia. Proporciona la forma adecuada de actuar, al favorecer la comprension del proceso y facilitar la resolucion de los problemas. Por otra parte, debido a su rico contenido en tablas, formularios y desarrollos, una vez asimiladas las ideas basicas, posee una utilidad posterior como libro de consulta facil, rapida y eficaz. Sin duda constituye una obra unica en su genero, cuyo valor puede considerarse permanente.

El libro que tiene en sus manos le proporciona un amplio panorama de modelos de probabilidad y métodos estadísticos; a través de ejemplos frescos y cuidadosamente seleccionados y graduados en el nivel de dificultad. Ahora; en la séptima edición; el autor presenta además una fuerte cobertura computacional con aplicaciones en SAS y MINITAB.Características * Contiene numerosos ejemplos basados en casos reales y con los datos auténticos * Las actividades a desarrollar tienen diferentes niveles de dificultad * El autor hace hincapié en que el estudiante aprenda a interpretar y no únicamente mecanice Estamos seguros de que no encontrará otra publicación igual en español que le ayude con tanta claridad a abordar y entender los diversos temas que conforman la probabilidad y estadística; así como a desarrollar su habilidad de análisis y solución de las actividades planteadas.

En este texto se estudian los principales conceptos de la Estadística con el propósito de que el lector aprenda los elementos básicos de esta materia y sepa aplicar sus métodos más habituales. Como para este último propósito es muy conveniente la utilización del ordenador, el texto se ha escrito ilustrando la exposición de los conceptos y métodos estadísticos con la ayuda de R, el paquete estadístico más utilizado.