templ matthias (85 resultados)

XIX, 287 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

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Paperback. Condición: New. Harness actionable insights from your data with computational statistics and simulations using RAbout This Book. Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies. A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulationWho This Book Is ForThis book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.What You Will Learn. The book aims to explore advanced R features to simulate data to extract insights from your data. Get to know the advanced features of R including high-performance computing and advanced data manipulation. See random number simulation used to simulate distributions, data sets, and populations. Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations. Applications to design statistical solutions with R for solving scientific and real world problems. Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.In DetailData Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world.The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results.By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these.

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Paperback. Condición: New. Harness actionable insights from your data with computational statistics and simulations using RAbout This Book. Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies. A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulationWho This Book Is ForThis book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.What You Will Learn. The book aims to explore advanced R features to simulate data to extract insights from your data. Get to know the advanced features of R including high-performance computing and advanced data manipulation. See random number simulation used to simulate distributions, data sets, and populations. Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations. Applications to design statistical solutions with R for solving scientific and real world problems. Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.In DetailData Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world.The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results.By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these.

Condición: acceptable. The book is complete and readable, with all pages and cover intact. Dust jacket, shrink wrap, or boxed set case may be missing. Pages may have light notes, highlighting, or minor water exposure, but nothing that affects readability. May be an ex-library copy and could include library markings or stickers.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results. The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the data before release. This book is intended for practitioners at statistical agencies and other national and international organizations that deal with confidential data. It will also be interesting for researchers working in statistical disclosure control and the health sciences.

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Condición: New. &Uumlber den AutorrnrnMatthias Templ is associated professor at the Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology (Austria). He is additionally employed as a scientist at the methods unit at Stat.

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Taschenbuch. Condición: Neu. Neuware -This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results.The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the data before release.This book is intended for practitioners at statistical agencies and other national and international organizations that deal with confidential data. It will also be interesting for researchers working in statistical disclosure control and the health sciences.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results. The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the data before release. This book is intended for practitioners at statistical agencies and other national and international organizations that deal with confidential data. It will also be interesting for researchers working in statistical disclosure control and the health sciences.

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