Active learning is a protocol for supervised machine learning in which a learning algorithm sequentially requests the labels of selected data points from a large pool of unlabeled data. This contrasts with passive learning where the labeled data are taken at random. The objective in active learning is to produce a highly-accurate classifier, ideally using fewer labels than the number of random labeled data sufficient for passive learning to achieve the same. Theory of Disagreement-Based Active Learning describes recent advances in our understanding of the theoretical benefits of active learning, and implications for the design of effective active learning algorithms. Much of the monograph focuses on a particular technique, namely disagreement-based active learning, which by now has amassed a mature and coherent literature. It also briefly surveys several alternative approaches from the literature. The emphasis is on theorems regarding the performance of a few general algorithms, including rigorous proofs where appropriate. However, the presentation is intended to be pedagogical, focusing on results that illustrate fundamental ideas rather than obtaining the strongest or most generally known theorems. Theory of Disagreement-Based Active Learning is intended for researchers and advanced graduate students in machine learning and statistics who are interested in gaining a deeper understanding of the recent and ongoing developments in the theory of active learning.

Computational Finance, an exciting new cross-disciplinary research area, depends extensively on the tools and techniques of computer science, statistics, information systems and financial economics for educating the next generation of financial researchers, analysts, risk managers, and financial information technology professionals. This new discipline, sometimes also referred to as "Financial Engineering" or "Quantitative Finance" needs professionals with extensive skills both in finance and mathematics along with specialization in computer science. Soft-Computing in Capital Market hopes to fulfill the need of applications of this offshoot of the technology by providing a diverse collection of cross-disciplinary research. This edited volume covers most of the recent, advanced research and practical areas in computational finance, starting from traditional fundamental analysis using algebraic and geometric tools to the logic of science to explore information from financial data without prejudice. Utilizing various methods, computational finance researchers aim to determine the financial risk with greater precision that certain financial instruments create. In this line of interest, twelve papers dealing with new techniques and/or novel applications related to computational intelligence, such as statistics, econometrics, neural- network, and various numerical algorithms are included in this volume.

Written in a friendly, Beginner's Guide format, showing the user how to use the digital media aspects of Matlab (image, video, sound) in a practical, tutorial-based style. This is great for novice programmers in any language who would like to use Matlab as a tool for their image and video processing needs, and also comes in handy for photographers or video editors with even less programming experience wanting to find an all-in-one tool for their tasks.

PROC REPORT by Example: Techniques for Building Professional Reports Using SAS provides real-world examples using PROC REPORT to create a wide variety of professional reports. Written from the point of view of the programmer who produces the reports, this book explains and illustrates creative techniques used to achieve the desired results. Each chapter focuses on a different concrete example, shows an image of the final report, and then takes you through the process of creating that report. You will be able to break each report down to find out how it was produced, including any data manipulation you have to do. The book clarifies solutions to common, everyday programming challenges and typical daily tasks that programmers encounter. For example: * obtaining desired report formats using style templates supplied by SAS and PROC TEMPLATE, PROC REPORT STYLE options, and COMPUTE block features * employing different usage options (DISPLAY, ORDER, GROUP, ANALYSIS, COMPUTED) to create a variety of detail and summary reports * using BREAK statements and COMPUTE blocks to summarize and report key findings * producing reports in various Output Delivery System (ODS) destinations including RTF, PDF, XML, TAGSETS.RTF * embedding images in a report and combining graphical and tabular data with SAS 9.2 and beyond Applicable to SAS users from all disciplines, the real-life scenarios will help elevate your reporting skills learned from other books to the next level. With PROC REPORT by Example: Techniques for Building Professional Reports Using SAS what seemed complex will become a matter of practice

A practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio. The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality to ease their development efforts. Familiarity with R is assumed. Those who want to get started with R development using RStudio will also find the book useful. Even if you already use R but want to create reproducible statistical analysis projects or extend R with self-written packages, this book shows how to quickly achieve this using RStudio.

"Numerical Methods using MATLAB, 3e, " is an extensive reference offering hundreds of useful and important numerical algorithms that can be implemented intoMATLAB for a graphical interpretation to help researchers analyze a particular outcome. Many worked examples are given together with exercises and solutions to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization, engineering and science across the board.

"Numerical Methods using MATLAB, 3e, " is an extensive reference offering hundreds of useful and important numerical algorithms that can be implemented intoMATLAB, to help researchers analyze a particular outcome. Many worked examples are given, together with exercises and solutions, to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization, engineering and science. Over 500 numerical algorithms, their fundamental principles, and applicationsGraphs are used extensively to clarify the complexity of problemsIncludes coded genetic algorithmsIncludes the Lagrange multiplier methodUser-friendly and written in a conversational style"

"Finite Difference Fundamentals in MATLAB" is devoted to the solution of numerical problems employing basic finite difference (FD) methods in MATLAB platform. FD is one momentous tool of numerical analysis on science and engineering problems. Advent of faster speed computer processors and user-friendliness of MATLAB have marvelously facilitated FD solution obtaining what is demonstrated in every chapter. Another aspect of the text is juxtaposition on computing and graphing features. The coverage narrates key executional MATLAB style of FD terminologies without arithmetic complexity. Self-training illustrations and end-of-chapter exercises inspire the reader a checkup on thorough understanding. The comprehensive introduction will benefit science and engineering undergraduates studying numerical analysis issues ranging archetype to advanced.

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. Heat Maps in R: How-to is an easy to understand book that starts with a simple heat map and takes you all the way through to advanced heat maps with graphics and data manipulation. Heat Maps in R How-to is the book for you if you want to make use of this free and open source software to get the most out of your data analysis. You need to have at least some experience in using R and know how to run basic scripts from the command line. However, knowledge of other statistical scripting languages such as Octave, S-Plus, or MATLAB will suffice to follow along with the recipes. You need not be from a statistics background.

This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.

Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with "JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition."

Updated for JMP 10 and including new features on the statistical platforms, this book offers clearly written instructions to guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results clearly in a way suitable for publication, perform a wide range of JMP analyses, interpret the results, and more.

Topics include screening data for errors selecting subsets computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale performing bivariate analyses for all types of variables performing a one-way analysis of variance (ANOVA), multiple regression, and a one-way multivariate analysis of variance (MANOVA) Advanced topics include analyzing models with interactions and repeated measures. There is also comprehensive coverage of principle components with emphasis on graphical interpretation.

This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while the more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.

This textbook explores two distinct stochastic processes that evolve at random: weakly stationary processes and discrete parameter Markov processes. Building from simple examples, the authors focus on developing context and intuition before formalizing the theory of each topic. This inviting approach illuminates the key ideas and computations in the proofs, forming an ideal basis for further study. After recapping the essentials from Fourier analysis, the book begins with an introduction to the spectral representation of a stationary process. Topics in ergodic theory follow, including Birkhoff's Ergodic Theorem and an introduction to dynamical systems. From here, the Markov property is assumed and the theory of discrete parameter Markov processes is explored on a general state space. Chapters cover a variety of topics, including birth-death chains, hitting probabilities and absorption, the representation of Markov processes as iterates of random maps, and large deviation theory for Markov processes. A chapter on geometric rates of convergence to equilibrium includes a splitting condition that captures the recurrence structure of certain iterated maps in a novel way. A selection of special topics concludes the book, including applications of large deviation theory, the FKG inequalities, coupling methods, and the Kalman filter. Featuring many short chapters and a modular design, this textbook offers an in-depth study of stationary and discrete-time Markov processes. Students and instructors alike will appreciate the accessible, example-driven approach and engaging exercises throughout. A single, graduate-level course in probability is assumed.

Il libro nasce dall esigenza di coniugare esperienze e capacita procedurali diverse provenienti da vari ambiti disciplinari, quali l informatica e la statistica, al fine di ricercare ed individuare percorsi e relazioni legate alla conoscenza. In un contesto di business, la conoscenza scoperta puo avere un valore strategico per le aziende perche consente di aumentare i profitti, riducendo i costi oppure aumentando le entrate con il conseguente aumento del ROI. Il volume e rivolto sia a studenti universitari e ricercatori, che a professionisti e manager aziendali che vogliano approfondire gli aspetti algoritmici delle tecniche di Data mining: lo studio degli algoritmi e delle principali tecniche e essenziale per conoscere meglio come la tecnologia possa essere applicata ai diversi tipi di dati e quindi anche diverse problematiche di business. Il testo pone volutamente l attenzione sugli aspetti procedurali e di calcolo della metodologia, differenziandosi dagli altri testi in italiano che inquadrano puramente il contesto statistico. Il materiale esposto puo essere utile a quanti vogliano completare la loro formazione scientifica in questa disciplina. "

A dashboard is a collection of data visualization tools that provide the means to quickly get an overview of how an organization or a section of an organization is performing. Industries such as sales and manufacturing use dashboards extensively, but dashboards are quickly being adapted across all types of profit and non-profit organizations. THE DESIGN OF INFORMATION DASHBOARDS USING SAS is a nuts and bolts guide to building information dashboards using SAS software. The primary audience for this book is SAS programmers charged with developing dashboards for their organization. This audience would include data managers, report writers, and business analysts. A secondary audience includes business mangers and non-programmers who are just hoping to learn a little more about the potential of the technology. The first four chapters provide background on the science of dashboards and related concepts. The remaining chapters cover coding and design of dashboard elements using SAS software. By providing clear, well-structured examples, the volume shows the reader how to quickly and easily construct basic dashboards that are suitable to their unique needs and environment. SAS users familiar with the basics of SAS and the fundamentals of SAS/GRAPH software will be able to make small changes to the sample code contained in the book to design simple dashboards. Advanced users with more extensive knowledge of SAS/GRAPH and the annotate facility will be able to more fully customize the sample code to fit a variety of needs. CHAPTER DESCRIPTIONS Chapter I. AN INTRODUCTION TO DASHBOARDS The first chapter defines precisely what dashboards are and their common characteristics. Following a brief history of information dashboards, the chapter discusses their value, as well as some negatives, and describes current use and trends. Finally, the value that SAS contributes to producing the medium is introduced. Chapter II. SEVEN STEPS TO CREATING A DASHBOARD The development of a dashboard often requires a substantial investment of time and money, so designers should do it thoughtfully. The goal of this chapter is to guide the reader through the dashboard development process. The chapter provides an overview of the major steps involved, including preparation, design, construction, and maintenance of dashboards. Chapter III. ESSENTIAL ELEMENTS OF A DASHBOARD When you create your dashboard, several essential elements should be present on the interface to make the dashboard maximally effective. The third chapter covers these essential components of a dashboard. Chapter IV. BEST PRACTICES IN DASHBOARD VISUAL DESIGN This chapter covers the foundations of good dashboard design and addresses the contributions of Edward Tufte and Stephen Few to the area. The chapter delves into the science of visual perception and how to apply them to good dashboard design. Chapter V. CREATING DASHBOARD KEY PERFORMANCE INDICATORS USING SAS The fifth chapter presents a library of effective dashboard display media and discusses how to produce them using SAS coding. Programmers will be able to pick and choose those chart types that are most appropriate for their particular dashboard. Strengths and weaknesses of the various chart types are discussed. This chapter will also introduces new SAS procedures such as PROC GKPI. Chapter VI. ASSEMBLING AND DISTRIBUTING SAS DASHBOARDS This chapter describes how to bring all the visual components together to produce a single dashboard display. PROC GREPLAY, ODSLAYOUT, and ODS TAGSETS are described as the methods of choice. Methods of distributing this output are described. Chapter VII. DESIGING DASHBOARDS USING SAS BI DASHBOARDS The final chapter briefly describes the design of dashboards using SAS BI Dashboards business intelligence software. For a limited time use the following code for 10% off your purchase on this site: F46FRNCS This title is also available for purchase on Amazon.com.

This easy-to-understand guide makes SEM accessible to all users. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.

Six Sigma statistical methodology using Minitab

"Problem Solving and Data Analysis using Minitab "presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving. Each example and exercise is broken down into the exact steps that must be followed in order to take the reader through key learning points and work through complex analyses. Exercises are featured at the end of each example so that the reader can be assured that they have understood the key learning points.

"Key features: "Provides readers with a step by step guide to problem solving and statistical analysis using Minitab 16 which is also compatible with version 15.Includes fully worked examples with graphics showing menu selections and Minitab outputs.Uses example based learning that the reader can work through at their pace.Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results.Presents the core statistical techniques used by Six Sigma Black Belts. Contains examples, exercises and solutions throughout, and is supported by an accompanying website featuring the numerous example data sets.

Making Six Sigma statistical methodology accessible to beginners, this book is aimed at numerical professionals, students or academics who wish to learn and apply statistical techniques for problem solving, process improvement or data analysis whilst keeping mathematical theory to a minimum.

Edward F. Vonesh's "Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS" is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.

"Applied Data Mining for Forecasting," by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large and identifies the correlation structure between selected candidate inputs and the forecast variable.

This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs.

Sanjay Matange and Dan Heath's "Statistical Graphics Procedures by Example: Effective Graphs Using SAS" shows the innumerable capabilities of SAS Statistical Graphics (SG) procedures. The authors begin with a general discussion of the principles of effective graphics, ODS Graphics, and the SG procedures. They then move on to show examples of the procedures' many features. The book is designed so that you can easily flip through it, find the graph you need, and view the code right next to the example.

Among the topics included are how to combine plot statements to create custom graphs; customizing graph axes, legends, and insets; advanced features, such as annotation and attribute maps; tips and tricks for creating the optimal graph for the intended usage; real-world examples from the health and life sciences domain; and ODS styles.

The procedures in "Statistical Graphics Procedures by Example" are specifically designed for the creation of analytical graphs. That makes this book a must-read for analysts and statisticians in the health care, clinical trials, financial, and insurance industries. However, you will find that the examples here apply to all fields.

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's "Logistic Regression Using SAS: Theory and Application, Second Edition," is for you Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing non-linear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models).

Statisticians and researchers will find "Categorical Data Analysis Using SAS, Third Edition," by Maura Stokes, Charles Davis, and Gary Koch, to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS. Practical examples from a broad range of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, NPAR1WAY, and CATMOD procedures in a variety of analyses. Topics discussed include assessing association in contingency tables and sets of tables, logistic regression and conditional logistic regression, weighted least squares modeling, repeated measurements analyses, loglinear models, generalized estimating equations, and bioassay analysis.

The third edition updates the use of SAS/STAT software to SAS/STAT 12.1 and incorporates ODS Graphics. Many additional SAS statements and options are employed, and graphs such as effect plots, odds ratio plots, regression diagnostic plots, and agreement plots are discussed. The material has also been revised and reorganized to reflect the evolution of categorical data analysis strategies. Additional techniques include such topics as exact Poisson regression, partial proportional odds models, Newcombe confidence intervals, incidence density ratios, and so on.

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.

Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's "Data Quality for Analytics Using SAS" focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.

With this book you will learn how you can use SAS to perform advanced profiling of data quality status and how SAS can help improve your data quality.

For anyone who wants to be operating at a high level with the Excel Solver quickly, this is the book for you. Step-By-Step Optimization With Excel Solver is more than 200+ pages of simple yet thorough explanations on how to use the Excel Solver to solve today's most widely known optimization problems. Loaded with screen shots that are coupled with easy-to-follow instructions, this book will simplify many difficult optimization problems and make you a master of the Excel Solver almost immediately. Here are just some of the Solver optimization problems that are solved completely with simple-to-understand instructions and screen shots in this book: The famous "Traveling Salesman" problem using Solver's Alldifferent constraint and the Solver's Evolutionary method to find the shortest path to reach all customers. This also provides an advanced use of the Excel INDEX function. The well-known "Knapsack Problem" which shows how optimize the use of limited space while satisfying numerous other criteria. How to perform nonlinear regression and curve-fitting on the Solver using the Solver's GRG Nonlinear solving method. How to solve the "Cutting Stock Problem" faced by many manufacturing companies who are trying to determine the optimal way to cut sheets of material to minimize waste while satisfying customer orders. Portfolio optimization to maximize return or minimize risk. Venture capital investment selection using the Solver's Binary constraint to maximize Net Present Value of selected cash flows at year 0. Clever use of the If-Then-Else statements makes this a simple problem. How use Solver to minimize the total cost of purchasing and shipping goods from multiple suppliers to multiple locations. How to optimize the selection of different production machine to minimize cost while fulfilling an order. How to optimally allocate a marketing budget to generate the greatest reach and frequency or number of inbound leads at the lowest cost. Step-By-Step Optimization With Excel Solver has complete instructions and numerous tips on every aspect of operating the Excel Solver. You'll fully understand the reports and know exactly how to tweek all of the Solver's settings for total custom use. The book also provides lots of inside advice and guidance on setting up the model in Excel so that it will be as simple and intuitive as possible to work with. All of the optimization problems in this book are solved step-by-step using a 6-step process that works every time. In addition to detailed screen shots and easy-to-follow explanations on how to solve every optimization problem in the book, a link is provided to download an Excel workbook that has all problems completed exactly as they are in this book. Step-By-Step Optimization With Excel Solver is exactly the book you need if you want to be optimizing at an advanced level with the Excel Solver quickly.