This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.

This book presents four mathematical essays which explore the foundations of mathematics and related topics ranging from philosophy and logic to modern computer mathematics. While connected to the historical evolution of these concepts, the essays place strong emphasis on developments still to come. The book originated in a 2002 symposium celebrating the work of Bruno Buchberger, Professor of Computer Mathematics at Johannes Kepler University, Linz, Austria, on the occasion of his 60th birthday. Among many other accomplishments, Professor Buchberger in 1985 was the founding editor of the Journal of Symbolic Computation; the founder of the Research Institute for Symbolic Computation (RISC) and its chairman from 1987-2000; the founder in 1990 of the Softwarepark Hagenberg, Austria, and since then its director. More than a decade in the making, Mathematics, Computer Science and Logic - A Never Ending Story includes essays by leading authorities, on such topics as mathematical foundations from the perspective of computer verification; a symbolic-computational philosophy and methodology for mathematics; the role of logic and algebra in software engineering; and new directions in the foundations of mathematics. These inspiring essays invite general, mathematically interested readers to share state-of-the-art ideas which advance the never ending story of mathematics, computer science and logic. Mathematics, Computer Science and Logic - A Never Ending Story is edited by Professor Peter Paule, Bruno Buchberger's successor as director of the Research Institute for Symbolic Computation.

The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.

This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.

This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets and the importance of interpreting displays effectively is emphasized. Graphics should be drawn to convey information and the book includes many insightful examples. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. The book is accessible to readers with some experience of drawing statistical graphics.

This proceedings volume contains eight selected papers that were presented in the International Symposium in Statistics (ISS) 2015 On Advances in Parametric and Semi-parametric Analysis of Multivariate, Time Series, Spatial-temporal, and Familial-longitudinal Data, held in St. John's, Canada from July 6 to 8, 2015. The main objective of the ISS-2015 was the discussion on advances and challenges in parametric and semi-parametric analysis for correlated data in both continuous and discrete setups. Thus, as a reflection of the theme of the symposium, the eight papers of this proceedings volume are presented in four parts. Part I is comprised of papers examining Elliptical t Distribution Theory. In Part II, the papers cover spatial and temporal data analysis. Part III is focused on longitudinal multinomial models in parametric and semi-parametric setups. Finally Part IV concludes with a paper on the inferences for longitudinal data subject to a challenge of important covariates selection from a set of large number of covariates available for the individuals in the study.

This book presents recent results on positivity and optimization of polynomials in non-commuting variables. Researchers in non-commutative algebraic geometry, control theory, system engineering, optimization, quantum physics and information science will find the unified notation and mixture of algebraic geometry and mathematical programming useful. Theoretical results are matched with algorithmic considerations; several examples and information on how to use NCSOStools open source package to obtain the results provided. Results are presented on detecting the eigenvalue and trace positivity of polynomials in non-commuting variables using Newton chip method and Newton cyclic chip method, relaxations for constrained and unconstrained optimization problems, semidefinite programming formulations of the relaxations and finite convergence of the hierarchies of these relaxations, and the practical efficiency of algorithms.

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed measurement levels data, sparse MCA, joint dimension reduction and clustering methods for categorical data, and acceleration of ALS computation. The variable selection methods in PCA that originally were developed for numerical data can be applied to any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the results of recent studies, are extensions obtained by the same matrix operations in nonlinear PCA. Finally, an acceleration algorithm is proposed to reduce the problem of computational cost in the ALS iteration in nonlinear multivariate methods. This book thus presents the usefulness of nonlinear PCA which can be applied to different measurement levels data in diverse fields. As well, it covers the latest topics including the extension of the traditional statistical method, newly proposed nonlinear methods, and computational efficiency in the methods.

Learn how to configure, implement, enhance, and customize SAP OEE to address manufacturing performance management. Manufacturing Performance Management using SAP OEE will show you how to connect your business processes with your plant systems and how to integrate SAP OEE with ERP through standard workflows and shop floor systems for automated data collection. Manufacturing Performance Management using SAP OEE is a must-have comprehensive guide to implementing SAP OEE. It will ensure that SAP consultants and users understand how SAP OEE can offer solutions for manufacturing performance management in process industries. With this book in hand, managing shop floor execution effectively will become easier than ever. Authors Dipankar Saha and Mahalakshmi Symsunder, both SAP manufacturing solution experts, and Sumanta Chakraborty, product owner of SAP OEE, will explain execution and processing related concepts, manual and automatic data collection through the OEE Worker UI, and how to enhance and customize interfaces and dashboards for your specific purposes. You'll learn how to capture and categorize production and loss data and use it effectively for root-cause analysis. In addition, this book will show you: Various down-time handling scenarios. How to monitor, calculate, and define standard as well as industry-specific KPIs. How to carry out standard operational analytics for continuous improvement on the shop floor, at local plant level using MII and SAP Lumira, and also global consolidated analytics at corporation level using SAP HANA. Steps to benchmark manufacturing performance to compare similar manufacturing plants' performance, leading to a more efficient and effective shop floor. Manufacturing Performance Management using SAP OEE will provide you with in-depth coverage of SAP OEE and how to effectively leverage its features. This will allow you to efficiently manage the manufacturing process and to enhance the shop floor's overall performance, making you the sought-after SAP OEE expert in the organization. What You Will Learn Configure your ERP OEE add-on to build your plant and global hierarchy and relevant master data and KPIs Use the SAP OEE standard integration (SAP OEEINT) to integrate your ECC and OEE system to establish bi-directional integration between the enterprise and the shop floor Enable your shop floor operator on the OEE Worker UI to handle shop floor production execution Use SAP OEE as a tool for measuring manufacturing performance Enhance and customize SAP OEE to suit your specific requirements Create local plant-based reporting using SAP Lumira and MII Use standard SAP OEE HANA analytics Who This Book Is For SAP MII, ME, and OEE consultants and users who will implement and use the solution.

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Matrix Algebra introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. Starting with a look at symbolic and numeric variables, with an emphasis on vector and matrix variables, you will go on to examine functions and operations that support vectors and matrices as arguments, including those based on analytic parent functions. Computational methods for finding eigenvalues and eigenvectors of matrices are detailed, leading to various matrix decompositions. Applications such as change of bases, the classification of quadratic forms and how to solve systems of linear equations are described, with numerous examples. A section is dedicated to sparse matrices and other types of special matrices. In addition to its treatment of matrices, you will also learn how MATLAB can be used to work with arrays, lists, tables, sequences and sets.

This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Linear Algebra introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to work in linear algebra with ease. In addition to exploring MATLAB's matrix algebra capabilities, it describes the MATLAB commands that are used to create two- and three-dimensional graphics, including explicit, implicit and parametric curve and surface plotting, and various methods of data representation. Methods for solving systems of equations are detailed.

Master the SAP product ecosystem, the client environment, and the feasibility of implementing critical business process with the required technical and functional configuration. SAP Project Management Pitfalls is the first book to provide you with real examples of the pitfalls that you can avoid, providing you with a road-map to a successful implementation. Jay Kay, a SAP Program Manager for Capgemini, first takes a deep dive into common pitfalls in implementing SAP ERP projects in a complex IT landscape. You will learn about the potential causes of failures, study a selection of relevant project implementation case studies in the area, and see a range of possible countermeasures. Jay Kay also provides background on each - the significance of each implementation area, its relevance to a service company that implements SAP projects, and the current state of research. Key highlights of the book: Tools and techniques for project planning and templates for allocating resources Industry standards and innovations in SAP implementation projects in the form of standard solutions aimed at successful implementation Managing SAP system ECC upgrades, EHP updates and project patches Learn effective ways to implement robust SAP release management practices (change management, BAU) Wearing a practitioner's insight, Jay Kay explores the relevance of each failed implementation scenario and how to support your company or clients to succeed in a SAP implementation. There are many considerations when implementing SAP, but as you will learn, knowledge, insight, and effective tools to mitigate risks can take you to a successful implementation project.

Take a deep dive into SAP Fiori and discover Fiori architecture, Fiori landscape installation, Fiori standard applications, Fiori Launchpad configuration, tools for developing Fiori applications and extending standard Fiori applications. You will learn: Fiori architecture and its applications Setting up a Fiori landscape and Fiori Launchpad Configuring, customizing and enhancing standard Fiori applications Developing Fiori native applications for mobile Internet of Things-based custom Fiori applications with the HANA cloud platform Bince Mathew, a SAP mobility expert working for an MNC in Germany, shows you how SAP Fiori, based on HTML5 technology, addresses the most widely and frequently used SAP transactions like purchase order approvals, sales order creation, information lookup, and self-service tasks. This set of HTML5 apps provides a very simple and accessible experience across desktops, tablets, and smartphones. Prerequisites and steps for setting up a Fiori landscape and Launchpad Fiori standard application configuration Extending and customizing standard Fiori applications Developing custom Fiori applications from scratch Building custom Fiori applications for Internet Of Things using HANA cloud Fiori apps with cordova and kapsel plugins

"Logistic Core Operations with SAP" not only provides an overview of core logistics processes and functionality-it also shows how SAP's Business Suite covers logistic core operations, what features are supported, and which systems can be used to implement end-to-end processes in the following logistic core disciplines: Procurement, Distribution, Transportation, Warehouse Logistics and Inventory Management, and Compliance and Reporting. In this context the authors not only explain their integration, the organizational set-up, and master data, but also which solution fits best for a particular business need. This book serves as a solid foundation for understanding SAP software. No matter whether you are a student or a manager involved in an SAP implementation, the authors go far beyond traditional function and feature descriptions, helping you ask the right questions, providing answers, and making recommendations. The book assists you in understanding SAP terminology, concepts and technological components as well as their closed-loop integration. Written in a clear, straight-forward style and using practical examples, it contains valuable tips, illustrative screenshots and flowcharts, as well as best practices-showing how business requirements are mapped into software functionality.

Gesch ftsprozesse in Unternehmen sind h ufig ereignisgesteuert. Denn im Gesch ftsumfeld treten Ereignisse auf, auf die angemessen und m glichst in Echtzeit reagiert werden muss, etwa in Sensornetzwerken oder im automatischen Wertpapierhandel. Event-Driven Architecture (EDA) ist ein neues Paradigma der Softwarearchitektur, das auf der Verarbeitung von Ereignissen beruht. Das Buch diskutiert die Grundprinzipien von EDA, f hrt in die wichtigsten Konzepte der Ereignisverarbeitung ein und veranschaulicht deren Umsetzung anhand einer Fallstudie.

The development of software system with acceptable level of reliability and quality within available time frame and budget becomes a challenging objective. This objective could be achieved to some extent through early prediction of number of faults present in the software, which reduces the cost of development as it provides an opportunity to make early corrections during development process. The book presents an early software reliability prediction model that will help to grow the reliability of the software systems by monitoring it in each development phase, i.e. from requirement phase to testing phase. Different approaches are discussed in this book to tackle this challenging issue. An important approach presented in this book is a model to classify the modules into two categories (a) fault-prone and (b) not fault-prone. The methods presented in this book for assessing expected number of faults present in the software, assessing expected number of faults present at the end of each phase and classification of software modules in fault-prone or no fault-prone category are easy to understand, develop and use for any practitioner. The practitioners are expected to gain more information about their development process and product reliability, which can help to optimize the resources used.

In der deutschen Volkswirtschaft entstehen ca. 90% der Wertschopfung durch Informationsverarbeitung und Kommunikation an elektronisch unterstutzten Arbeitsplatzen. Aspekte wie Arbeitsorganisation, Kommunikationsprozessgestaltung, Ergonomie, Buroraumgestaltung, Motivation, Fuhrung, Strategie und I&K-Ausstattung beeinflussen das komplexe System Arbeitsplatz und damit die mogliche Wertschopfung.

In diesem Buch werden die wesentlichen Aspekte des Themas Arbeitsplatzgestaltung betrachtet: Nach einer Klarung der Bedeutung der Arbeits(platz)gestaltung fur die Wertschopfung eines Unternehmens werden die kurz- und mittelfristig relevanten optionalen Konzepte elektronischer Kommunikation und Zusammenarbeit vorgestellt. Alle diskutierten Konzepte werden mit Fallstudien abgeschlossen, die dem Leser aufzeigen, welche Bedeutung das jeweilige Konzept fur sein Unternehmen haben konnte. Abschliessend werden die Themen diskutiert, mit denen sich jeder zwangslaufig beschaftigen muss, der zum Thema Arbeits(platz)gestaltung Verantwortung tragt. Dabei werden Empfehlungen zu Knowledge Management und Prozessoptimierung gegeben."

Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto< tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher's website.

Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3. R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research.What You Will Learn: How to acquire and install R Hot to import and export data and scripts How to analyze data and generate graphics How to program in R to write custom functions Hot to use R for interactive statistical explorations How to conduct bootstrapping and other advanced techniques