Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous time processes and numerical option pricing. Introduces the bases of probability theory and goes on to explain how to model financial times series with continuous models, how to calibrate them from discrete data and further covers option pricing with one or more underlying assets based on these models. Analysis and implementation of models goes beyond the standard Black and Scholes framework and includes Markov switching models, Lévy models and other models with jumps (e.g. the telegraph process); Topics other than option pricing include: volatility and covariation estimation, change point analysis, asymptotic expansion and classification of financial time series from a statistical viewpoint. The book features problems with solutions and examples. All the examples and R code are available as an additional R package, therefore all the examples can be reproduced.

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data "Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author

How do you use R to import, manage, visualize, and analyze real-world data? With this short, hands-on tutorial, you learn how to collect online data, massage it into a reasonable form, and work with it using R facilities to interact with web servers, parse HTML and XML, and more. Rather than use canned sample data, you'll plot and analyze current home foreclosure auctions in Philadelphia. This practical mashup exercise shows you how to access spatial data in several formats locally and over the Web to produce a map of home foreclosures. It's an excellent way to explore how the R environment works with R packages and performs statistical analysis. Parse messy data from public foreclosure auction postings Plot the data using R's PBSmapping package Import US Census data to add context to foreclosure data Use R's lattice and latticeExtra packages for data visualization Create multidimensional correlation graphs with the pairs() scatterplot matrix package

R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression. You'll find recipes on reading data files, creating data frames, computing basic statistics, testing means and correlations, creating a scatter plot, performing simple linear regression, and many more. These solutions were selected from O'Reilly's , which contains more than 200 recipes for R that you'll find useful once you move beyond the basics. R Cookbook

Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics. The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems. Understand the basics of the language, including the nature of R objects Learn how to write R functions and build your own packages Work with data through visualization, statistical analysis, and other methods Explore the wealth of packages contributed by the R community Become familiar with the lattice graphics package for high-level data visualization Learn about bioinformatics packages provided by Bioconductor "I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians."

This article demonstrates how the realworld data is imported, managed, visualized, and analyzed within the R statistical framework. Presented as a spatial mashup, this tutorial introduces the user to R packages, R syntax, and data structures. The user will learn how the R environment works with R packages as well as its own capabilities in statistical analysis. We will be accessing spatial data in several formats-html, xml, shapefiles, and text-locally and over the web to produce a map of home foreclosure auctions and perform statistical analysis on these events.

Bringing together all the relevant material to impart a clear understanding of how to analyze ROC curves, this book covers the fundamental theory as well as various special topics. It provides illustrative examples of the major methodological developments and includes as much of the mathematical theory as necessary without making the treatment too dense. The authors survey the uses made of the methodology across a range of different areas, from atmospheric science and geoscience to experimental psychology and sociology. They also list a number of websites from which software implementing the various techniques can be downloaded.

The first textbook of its kind, Quantitative Corpus Linguistics with R demonstrates how to use the open source programming language R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to achieve – searching and processing corpora, arranging and outputting the results of corpus searches, statistical evaluation, and graphing.

Written by a member of the R Development Core Team, this resource provides important information on how to work with research data. It contains a collection of diverse, computer-related topics, connecting them through numerous, real-world case studies. The author describes open source technologies and open standards and devotes separate chapters to each computer language, including HTML, XML, SQL, and R. Explanatory diagrams aid in understanding important concepts, helping readers perform research tasks with ease. In addition, the author's website includes a suite of exercises as well as the code and data sets used in the case studies.

Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems. Drawing on the author’s first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code. With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.

The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the author's bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines. Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities. Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.

The Resource Description Framework (RDF) is a structure for describing and interchanging metadata on the Web--anything from library catalogs and worldwide directories to bioinformatics, Mozilla internal data structures, and knowledge bases for artificial intelligence projects. RDF provides a consistent framework and syntax for describing and querying data, making it possible to share website descriptions more easily. RDF's capabilities, however, have long been shrouded by its reputation for complexity and a difficult family of specifications. Practical RDF breaks through this reputation with immediate and solvable problems to help you understand, master, and implement RDF solutions. Practical RDF explains RDF from the ground up, providing real-world examples and descriptions of how the technology is being used in applications like Mozilla, FOAF, and Chandler, as well as infrastructure you can use to build your own applications. This book cuts to the heart of the W3C's often obscure specifications, giving you tools to apply RDF successfully in your own projects.The first part of the book focuses on the RDF specifications. After an introduction to RDF, the book covers the RDF specification documents themselves, including RDF Semantics and Concepts and Abstract Model specifications, RDF constructs, and the RDF Schema. The second section focuses on programming language support, and the tools and utilities that allow developers to review, edit, parse, store, and manipulate RDF/XML. Subsequent sections focus on RDF's data roots, programming and framework support, and practical implementation and use of RDF and RDF/XML.If you want to know how to apply RDF to information processing, Practical RDF is for you. Whether your interests lie in large-scale information aggregation and analysis or in smaller-scale projects like weblog syndication, this book will provide you with a solid foundation for working with RDF.

Real R&D options are among the earliest modelled real options, with now ten primary practical uses: general R&D planning, planning R&D in stages, evaluating test information, new product development timing, operations, abandonment, risk sharing, market funding, industry strategy and regulation. This book was partly motivated by requests to identify and develop real option models for R&D in telecommunications, petroleum technology and biotechnology. Nine new models cover information and implementation costs, analytical solutions for mean reverting, or fat tailed revenues, endogenous learning and exogenous and experiential shocks, American sequential options, and innovator advantages. Four new applications include forward start development options, exploration options, innovation with information costs, and innovator's real values with changing market share. R&D directors and researchers will find several uses for these models: · general R&D planning · evaluating test information · new product development timing · risk sharing · industry strategy and regulation A practical guide to how organizations can use Real Option techniques to effectively value research and development by companies Provides a rigorous theoretical underpinning of the use of Real Option techniques *Real Options applications are orientated around the economies of North America, Europe and Asia, for an international perspective