This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide va…
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted B…
Through analysis of the European Union Emissions Trading Scheme (EU ETS) and the Clean Development Mechanism (CDM), this book demonstrates how to use a variety of econometric techniques to analyze the evolving and expanding carbon markets sphere, techniques that can be extrapola…
Das Übungsbuch stellt eine ausgesuchte Sammlung von Problemstellungen und Lösungen bereit, die durch eine Formelsammlung mit den wichtigsten im Buch verwendeten Formeln abgerundet wird. Zusätzlich wird ein umfangreiches Set von Programmen in R zur Verfügung gestellt, die zur Auf…
Résumé : <p>R is a powerful and free software system for data analysis and graphics, with over 4,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and…
R is a powerful and free software system for data analysis and graphics, with over 4,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and co…
Résumé : <p>This is the first book to show the capabilities of Microsoft Excel to teach business statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical business problems. If understanding stat…
Les techniques informatiques de simulation sont essentielles au statisticien. Afin que celui-ci puisse les utiliser en vue de résoudre des problèmes statistiques, il lui faut au préalable développer son intuition et sa capacité à produire lui-même des modèles de simulation. Ce l…
<P>The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is no…
Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and te…