Modelo Lineal Generalizado Espacial Con Variable Respuesta Beta
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Modelo Lineal Generalizado Espacial Con Variable Respuesta Beta
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Author : Roberto Fustos Toribio
language : es
Publisher:
Release Date : 2013
Modelo Lineal Generalizado Espacial Con Variable Respuesta Beta written by Roberto Fustos Toribio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Modelos Lineales Generalizados Ejercicios Resueltos Con R Sas Stata Eviews Spss Y Statgraphics
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Author : Cesar Perez
language : es
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-07-16
Modelos Lineales Generalizados Ejercicios Resueltos Con R Sas Stata Eviews Spss Y Statgraphics written by Cesar Perez and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-16 with categories.
El modelo lineal generalizado amplía el modelo lineal general, de manera que la variable dependiente y está relacionada linealmente con los factores y las covariables mediante una determinada función de enlace . Además, el modelo permite que la variable dependiente tenga una distribución no normal. El modelo lineal generalizado cubre los modelos estadísticos más utilizados, como la regresión lineal para las respuestas distribuidas normalmente, modelos logísticos para datos binarios, modelos loglineales para datos de recuento, modelos log-log complementario para datos de supervivencia censurados por intervalos, además de muchos otros modelos estadísticos a través de la propia formulación general del modelo.La posibilidad de especificar una distribución específica para la variable dependiente que no sea la normal y la posibilidad de especificar una función de enlace que no sea la identidad, es la principal mejora que aporta el modelo lineal generalizado respecto al modelo lineal general. Si la distribución de la variable dependiente es normal y la función de enlace es la identidad estamos ante el modelo lineal general.
Modelo Lineal Generalizado
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Author : Manuel Ato García
language : es
Publisher:
Release Date : 2005
Modelo Lineal Generalizado written by Manuel Ato García and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Education categories.
El MLG (Modelo lineal generalizado) constituye la generalización natural de los Modelos Lineales clásicos, e incluye como casos particulares la regresión lineal, el análisis de varianza, el análisis de la covarianza, la regresión logit, la regresión de Poisson, los modelos log-lineales, los modelos de respuesta multinomial, así como ciertos modelos de análisis de supervivencia y de series temporales.
Modelos Estad Sticos Lineales Con Aplicaciones En R
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Author : Cristian Fernando Tellez
language : es
Publisher: Ediciones de la U
Release Date : 2016-01-01
Modelos Estad Sticos Lineales Con Aplicaciones En R written by Cristian Fernando Tellez and has been published by Ediciones de la U this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-01 with Education categories.
Este libro implementa rápidamente en el lector el pensamiento estadístico en términos de la modelación estocástica. Los autores plasman los principales resultados de la teoría clásica de una forma estructurada, clara y precisa. Desde sus primeras páginas, este texto muestra rigurosamente el desarrollo de deducciones primordiales y con algunos ejemplos léxicos gráficos se ilustran las propiedades numéricas de forma exhaustiva. Dentro de cada capítulo, el lector podrá encontrar ejemplos empíricos y simulaciones basadas en el software computacional R. Sin embargo, la claridad de los contenidos expuestos hace que la programación de rutinas computacionales permanentes sea muy expedita de realizar con otros paquetes diferentes a R.
Big Data T Cnicas Herramientas Y Aplicaciones
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Author : María PÉREZ MARQUÉS
language : es
Publisher: Alfaomega Grupo Editor
Release Date : 2015-07-24
Big Data T Cnicas Herramientas Y Aplicaciones written by María PÉREZ MARQUÉS and has been published by Alfaomega Grupo Editor this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-24 with Business & Economics categories.
En la era de grandes conjuntos de datos, procedentes de diversos orígenes, en formatos variados y con una necesidad de procesamiento y análisis rápido y efectivo, las técnicas de Big Data persiguen complementar el manejo ordenado de estos volúmenes, con las técnicas de análisis de la información más avanzadas y efectivas para extraer de modo óptimo el conocimiento contenido en los datos. Las herramientas de Big Data se basan en el paquete de código abierto llamado Hadoop para el análisis masivo de datos, que forma parte de prácticamente todo el software de Big Data. Por ejemplo, SAS incorpora Hadoop en sus aplicaciones (SAS Base, SAS Data Integration, SAS Visual Analytics, SAS Visual Statistics, etc.). IBM trabaja con Hadoop en su plataforma IBM InfoSphere BigInsights. Microsoft incluye Hadoop en su plataforma Windows Azure, SQL Server 2014, HDInsight y Polybase. Oracle incluye Hadoop en Oracle Big Data Appliance, Oracle Big Data Connectors y Oracle Loader for Hadoop. Se describen y analizan estas herramientas de Big Data que implementan SAS, IBM, Microsoft y Oracle, para extraer el conocimiento contenido en los datos. Ventajas: · Los principales comandos Conozca: · Las herramientas de BIG DATA, que utilizan tecnologías multinúcleo para ofrecer mayor capacidad de procesamiento a través de altas prestaciones, en base de datos y de análisis en memoria que ofrecen un mayor conocimiento más rápidamente de grandes volúmenes de datos y flujo de datos, independientemente de los formatos y las fuentes de los orígenes de datos. Aprenda: ·Que con las herramientas de BIG DATA se puede procesar información online proveniente de múltiples orígenes (redes sociales o grandes bases de datos no estructuradas), · A tratar los datos de múltiples fuentes y formatos, ya sean texto, datos, imágenes o mezcla de todo ello. Actualmente es posible. Desarrolle sus habilidades para: · Implementar herramientas de BIG DATA en la forma que mejor se adapte a las necesidades de los usuarios. · Superar con éxito el desafío del análisis de la información, dada la capacidad de almacenar cualquier cosa, lo que están generando datos como nunca antes en la historia
Probability And Statistics For Engineering And The Sciences Enhanced Webassign Access
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Author :
language : en
Publisher:
Release Date : 2017
Probability And Statistics For Engineering And The Sciences Enhanced Webassign Access written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Applied Linear Statistical Models
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Author : Michael H. Kutner
language : en
Publisher: McGraw-Hill/Irwin
Release Date : 2005
Applied Linear Statistical Models written by Michael H. Kutner and has been published by McGraw-Hill/Irwin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Mathematics categories.
Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.
Introduction To Econometrics
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Author : G. S. Maddala
language : en
Publisher:
Release Date : 2001-05-08
Introduction To Econometrics written by G. S. Maddala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-05-08 with Business & Economics categories.
Introduction to Econometrics has been significantly revised to include new developments in the field. The previous editions of this text were renowned for Maddala's clear exposition and the presentation of concepts in an easily accessible manner. Features: * New chapters have been included on panel data analysis, large sample inference and small sample inference * Chapter 14 Unit Roots and Cointegration has been rewritten to reflect recent developments in the Dickey-Fuller (DF), the Augmented Dickey-Fuller (ADF) tests and the Johansen procedure * A selection of data sets and the instructor's manual for the book can be found on our web site Comments on the previous edition: 'Maddala is an outstanding econometrician who has a deep understaning of the use and potential abuse of econometrics...' 'The strengths of the Maddala book are its simplicity, its accessibility and the large number of examples the book contains...' 'The second edition is well written and the chapters are focused and easy to follow from beginning to end. Maddala has an oustanding grasp of the issues, and the level of mathematics and statistics is appropriate as well.'
Statistics For Spatial Data
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Author : Noel Cressie
language : en
Publisher: John Wiley & Sons
Release Date : 2015-03-18
Statistics For Spatial Data written by Noel Cressie and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-18 with Mathematics categories.
The Wiley Classics Library consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. Spatial statistics — analyzing spatial data through statistical models — has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific literature, learning how to make practical use of spatial statistics in day-to-day analytical work is very difficult. Designed exclusively for scientists eager to tap into the enormous potential of this analytical tool and upgrade their range of technical skills, Statistics for Spatial Data is a comprehensive, single-source guide to both the theory and applied aspects of spatial statistical methods. The hard-cover edition was hailed by Mathematical Reviews as an "excellent book which will become a basic reference." This paper-back edition of the 1993 edition, is designed to meet the many technological challenges facing the scientist and engineer. Concentrating on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, revealing how design, inference, and diagnostics are an outgrowth of that link. It then explores new methods to reveal just how spatial statistical models can be used to solve important problems in a host of areas in science and engineering. Discussion includes: Exploratory spatial data analysis Spectral theory for stationary processes Spatial scale Simulation methods for spatial processes Spatial bootstrapping Statistical image analysis and remote sensing Computational aspects of model fitting Application of models to disease mapping Designed to accommodate the practical needs of the professional, it features a unified and common notation for its subject as well as many detailed examples woven into the text, numerous illustrations (including graphs that illuminate the theory discussed) and over 1,000 references. Fully balancing theory with applications, Statistics for Spatial Data, Revised Edition is an exceptionally clear guide on making optimal use of one of the ascendant analytical tools of the decade, one that has begun to capture the imagination of professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology.
Mathematical Statistics With Applications In R
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Author : Kandethody M. Ramachandran
language : en
Publisher: Elsevier
Release Date : 2014-09-14
Mathematical Statistics With Applications In R written by Kandethody M. Ramachandran and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-14 with Mathematics categories.
Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students.Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods