Non-uniform random variate generation is an established research area in the intersection of mathematics, statistics and computer science. Although random variate generation with popular standard distributions have become part of every course on discrete event simulation and on Monte Carlo methods, the recent concept of universal (also called automatic or black-box) random variate generation can only be found dispersed in literature. This new concept has great practical advantages that are little known to most simulation practitioners. Being unique in its overall organization the book covers not only the mathematical and statistical theory, but also deals with the implementation of such methods. All algorithms introduced in the book are designed for practical use in simulation and have been coded and made available by the authors. Examples of possible applications of the presented algorithms (including option pricing, VaR and Bayesian statistics) are presented at the end of the book.

SharePoint 2010 offers developers, designers, and users a sophisticated intranet, web application, and Internet site platform. But until now, SharePoint users have had to painfully edit code or seek developer assistance to customize more than just the most minor parts of their sites. All of that has changed with SharePoint Designer 2010. Gain complete control and enhance your SharePoint sites with a depth not previously possible using this book.Pro SharePoint Designer 2010 isyour soup-to-nuts reference for unlocking the power of SharePoint Designer.
Covering everything you'll need to know to create custom, rich SharePoint experiences, Pro SharePoint Designer 2010 is a masterful guide to getting the most from this powerful application quickly and easily.
This book starts with a fast-paced introduction to the 2010 version of the SharePoint Designer solution, including an overview of its features and capabilities. Then, the authorsdemonstrate those tools inaction in a practical, results-oriented way, showing you howto vastly improve the functionality, fit and finish of your SharePoint sites. With Pro SharePoint Designer 2010 at hand, you'll master the customization of the end user's completeSharePoint 2010 experience and be on your way to enhancing your sites in no time. What you'll learn How to create and edit master pages and page layouts How to make, customize and enhance libraries and lists How to exercise granular control of site permissions on all SharePoint 2010 objects How to enhance the look and feel of forms, library views, and more, through CSS styles and themes How to connect SharePoint to databases, web applications, line-of-business (LOB) programs, and more How to integrate InfoPath for automated form handling and control How to create and manage web parts How to further customize SharePoint using JavaScript and Silverlight Who this book is for

This book is for end users, administrators, and novice developers with some coding experience, and anyone else who wants to create custom, rich SharePoint experiences quickly and easily. More advanced users will find the client-side programming material helpful as well. Table of Contents A Quick Guide to SharePoint Designer Editing Pages Using SharePoint to Store Data Managing Web Parts Displaying Data Styles and Themes Managing Publishing Sites Advanced Site Customizations Client-Side Programming Consuming External Data Using InfoPath Forms Automating with Workflows

This brief offers a broad, yet concise, coverage of portfolio choice, containing both application-oriented and academic results, along with abundant pointers to the literature for further study. It cuts through many strands of the subject, presenting not only the classical results from financial economics but also approaches originating from information theory, machine learning and operations research. This compact treatment of the topic will be valuable to students entering the field, as well as practitioners looking for a broad coverage of the topic.

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568.

The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced.

Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors' highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

This is the sixth volume in a series dealing with such topics as information systems practice and theory, information systems and the accounting/auditing environment, and differing perspectives on information systems research.

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.

Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R.

Focusing on the R software, the first section covers:

• Basic elements of the R software and data processing
• Clear, concise visualization of results, using simple and complex graphs
• Programming basics: pre-defined and user-created functions

The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including:

• Regression methods
• Analyses of variance and covariance
• Classification methods
• Exploratory multivariate analysis
• Clustering methods
• Hypothesis tests

After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.

Datasets and all the results described in this book are available on the book s webpage at http: //www.agrocampus-ouest.fr/math/RforStat

Keep projects on track Microsoft Project 2019 is a powerhouse project management, portfolio management, and resource management tool. Whether you're a full-time project manager or manage projects as part of a larger set of duties, Microsoft Project 2019 For Dummies will get you thinking and operating at the level of a project management guru. Written by a noted project management pro, this book covers the ins and outs of Microsoft Project. Throughout the book, you'll find project management best practices and tips for keeping any project on schedule and under budget. Reference the full set of Microsoft Project 2019 features Learn to think like a project management professional Get into the nuts and bolts of Project for better productivity Create a task schedule that keeps a project moving Identify the golden rules that keep projects on track With Microsoft Project 2019 For Dummies, you'll soon get a grip on all the powerful features of this popular project management software. No matter your level of training or experience, this book will show you how improve your project management with Microsoft Project 2019.

R Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages. In this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ioslides/Slidy/Beamer/PowerPoint presentations Extensions and applications: Dashboards, Tufte handouts, xaringan/reveal.js presentations, websites, books, journal articles, and interactive tutorials Advanced topics: Parameterized reports, HTML widgets, document templates, custom output formats, and Shiny documents. Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages, including knitr, rmarkdown, bookdown, blogdown, shiny, xaringan, and animation. He has published three other books, Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, and blogdown: Creating Websites with R Markdown. J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown ecosystem including rmarkdown, flexdashboard, learnr, and radix. Garrett Grolemund is the co-author of R for Data Science and author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as an advocate who trains engineers to do data science with R and the Tidyverse.

PowerPivot comprises a set of technologies for easy access to data mining and business intelligence analysis from Microsoft Excel and SharePoint. Power users and developers alike can create sophisticated, online analytic processing (OLAP) solutions using PowerPivot for Excel, and then share those solutions with other users via PowerPivot for SharePoint. Data can be pulled in from any of the leading database platforms, as well as from spreadsheets and flat files PowerPivot for Business Intelligence Using Excel and SharePointis your key to mastering PowerPivot. The book takes a scenario-based approach to showing you how to collect data, to mine that data through insightful analysis, and to draw conclusions that drive business performance. Each chapter in the book is focused on a specific challenge that you'll encounter when using PowerPivot. Each chapter takes you through a solution technique that's been proven in the real world. Covers the leading technology for bringing data analytics to the desktop Presents real-world solutions to real-world scenarios Written by a Microsoft Virtual Technical Specialist (VTS) for business intelligence What you'll learn Install and verify the PowerPivot software Integrated existing, available data to deliver business intelligence Leverage Time Intelligence to report change over time Write Data Analysis Expressions (DAX) to create custom measures Identify and implement solutions for role-playing dimensions Recognize and work-around PowerPivot's missing features Who this book is for

PowerPivot Solutions for Excel and SharePoint is aimed at information workers and data analysts who typically use Excel to drive business decisions. The book shows how you can apply PowerPivot to problems typically addressed through complicated and arcane spreadsheet techniques. Business people without the time and interest in learning Excel arcane will especially appreciate how PowerPivot enables them to easily create models and perform analysis far in advance of anything they could do using Excel alone. Table of Contents Getting Started with PowerPivot for Excel Hello World, PowerPivot Style Combining Data Sources Data Analysis Expressions A Method to the Madness Installing PowerPivot for SharePoint Collaboration, Version Control, and Management PowerPivot As a Data Source PowerPivot and SQL Server Reporting Services PowerPivot and Predictive Analytics Tips, Tricks, and Traps

There's a lot more to the blockchain than mining Bitcoin. This secure system for registering and verifying ownership and identity is perfect for supply chain logistics, health records, and other sensitive data management tasks. Blockchain in Action unlocks the full potential of this revolutionary technology, showing you how to build own decentralized apps for secure applications including digital democracy, private auctions, and electronic record management. Key Features * How blockchain differs from other distributed systems * Smart contract development with Ethereum and the Solidity language * Web UI for decentralized apps * Identity, privacy and security techniques * On-chain and off-chain data storage For intermediate programmers who know the basics of object-oriented languages and have a working knowledge of JavaScript. About the technology A blockchain is a decentralized record, stored across numerous devices with no central control or authority. Copies of this shared database are constantly reconciled with one another, and records are cryptographically encoded to make them unchangeable. The result is a type of database that is at once transparent and publicly accessible, and where it is impossible to falsify or alter the historic data record. Bina Ramamurthy holds a Ph.D. in fault-tolerant distributed systems, and has thirty years of experience teaching cryptography, peer-to-peer networking, and distributed systems. She is the instructor and content creator for the University of Buffalo four-course specialization on blockchain technology on the Coursera MOOC platform, and the recipient of the 2019 SUNY Chancellor's Award for Teaching Excellence.

Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems. After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.

The articles that comprise this distinguished annual volume for the Advances in Mechanics and Mathematics series have been written in honor of Gilbert Strang, a world renowned mathematician and exceptional person. Written by leading experts in complementarity, duality, global optimization, and quantum computations, this collection reveals the beauty of these mathematical disciplines and investigates recent developments in global optimization, nonconvex and nonsmooth analysis, nonlinear programming, theoretical and engineering mechanics, large scale computation, quantum algorithms and computation, and information theory.

Each chapter consists of basic statistical theory, simple examples of S-PLUS code, plus more complex examples of S-PLUS code, and exercises. All data sets are taken from genuine medical investigations and will be available on a web site. The examples in the book contain extensive graphical analysis to highlight one of the prime features of S-PLUS. Written with few details of S-PLUS and less technical descriptions, the book concentrates solely on medical data sets, demonstrating the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.

The theory of U-statistics goes back to the fundamental work of Hoeffding 1], in which he proved the central limit theorem. During last forty years the interest to this class of random variables has been permanently increasing, and thus, the new intensively developing branch of probability theory has been formed. The U-statistics are one of the universal objects of the modem probability theory of summation. On the one hand, they are more complicated "algebraically" than sums of independent random variables and vectors, and on the other hand, they contain essential elements of dependence which display themselves in the martingale properties. In addition, the U -statistics as an object of mathematical statistics occupy one of the central places in statistical problems. The development of the theory of U-statistics is stipulated by the influence of the classical theory of summation of independent random variables: The law of large num bers, central limit theorem, invariance principle, and the law of the iterated logarithm we re proved, the estimates of convergence rate were obtained, etc."

This book provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, pseudocode, and well-developed examples for applications of the branch-and-bound paradigm to important problems in combinatorial data analysis.

Supplementary material, such as computer programs, are provided on the world wide web.

Dr. Brusco is an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America.

This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover stochastic differential equations, the concentration here on inference makes this book stand out. No other direct competitors are known to date. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners and students with minimal mathematical background. What's more, because of the many R programs, the information here is appropriate for many mathematically well educated practitioners, too.

There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield ..".students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.

Most global optimization literature focuses on theory. This book, however, contains descriptions of new implementations of general-purpose or problem-specific global optimization algorithms. It discusses existing software packages from which the entire community can learn. The contributors are experts in the discipline of actually getting global optimization to work, and the book provides a source of ideas for people needing to implement global optimization software.

Over the last few decades, important progresses in the methods of sampling have been achieved. This book draws up an inventory of new methods that can be useful for selecting samples. Forty-six sampling methods are described in the framework of general theory. The algorithms are described rigorously, which allows implementing directly the described methods. This book is aimed at experienced statisticians who are familiar with the theory of survey sampling.

The first edition was released in 1996 and has sold close to 2200 copies.

Provides an up-to-date comprehensive treatment of MDS, a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space.

The authors have added three chapters and exercise sets. The text is being moved from SSS to SSPP.

The book is suitable for courses in statistics for the social or managerial sciences as well as for advanced courses on MDS.

All the mathematics required for more advanced topics is developed systematically in the text.

Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks.

Proceedings of the 19th international symposium on computational statistics, held in Paris august 22-27, 2010.Together with 3 keynote talks, there were 14 invited sessions and more than 100 peer-reviewed contributed communications.

Optical Scanning Holography is an exciting new field with many potential novel applications. This book contains tutorials, research materials, as well as new ideas and insights that will be useful for those working in the field of optics and holography. The book has been written by one of the leading researchers in the field. It covers the basic principles of the topic which will make the book relevant for years to come.

This book reviews some of today's more complex problems, and reflects some of the important research directions in the field. Twenty-nine authors - largely from Montreal's GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes - present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.