Statistical Computing
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| Author | : Jochen Voss |
| Publisher | : John Wiley & Sons |
| Total Pages | : 322 |
| Release | : 2013-08-28 |
| Genre | : Mathematics |
| ISBN | : 1118728025 |
A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: Fully covers the traditional topics of statistical computing. Discusses both practical aspects and the theoretical background. Includes a chapter about continuous-time models. Illustrates all methods using examples and exercises. Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online. Includes an introduction to programming in R. This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course.
| Author | : WIlliam J. Kennedy |
| Publisher | : Routledge |
| Total Pages | : 612 |
| Release | : 2021-06-23 |
| Genre | : Mathematics |
| ISBN | : 1351414585 |
In this book the authors have assembled the "best techniques from a great variety of sources, establishing a benchmark for the field of statistical computing." ---Mathematics of Computation ." The text is highly readable and well illustrated with examples. The reader who intends to take a hand in designing his own regression and multivariate packages will find a storehouse of information and a valuable resource in the field of statistical computing.
| Author | : Maria L. Rizzo |
| Publisher | : CRC Press |
| Total Pages | : 412 |
| Release | : 2007-11-15 |
| Genre | : Reference |
| ISBN | : 1420010719 |
Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditiona
| Author | : R.A. Thisted |
| Publisher | : Routledge |
| Total Pages | : 456 |
| Release | : 2017-10-19 |
| Genre | : Mathematics |
| ISBN | : 1351452746 |
Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques.
| Author | : Micah Altman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 349 |
| Release | : 2004-02-15 |
| Genre | : Mathematics |
| ISBN | : 0471475742 |
At last—a social scientist's guide through the pitfalls of modern statistical computing Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing. Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field. Highlights include: A focus on problems occurring in maximum likelihood estimation Integrated examples of statistical computing (using software packages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS, WinBUGS, and MATLAB®) A guide to choosing accurate statistical packages Discussions of a multitude of computationally intensive statistical approaches such as ecological inference, Markov chain Monte Carlo, and spatial regression analysis Emphasis on specific numerical problems, statistical procedures, and their applications in the field Replications and re-analysis of published social science research, using innovative numerical methods Key numerical estimation issues along with the means of avoiding common pitfalls A related Web site includes test data for use in demonstrating numerical problems, code for applying the original methods described in the book, and an online bibliography of Web resources for the statistical computation Designed as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field.
| Author | : Peter Dalgaard |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 370 |
| Release | : 2008-06-27 |
| Genre | : Mathematics |
| ISBN | : 0387790543 |
This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.
| Author | : Kenneth Lange |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 606 |
| Release | : 2010-05-17 |
| Genre | : Business & Economics |
| ISBN | : 1441959459 |
Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.
| Author | : Luke Tierney |
| Publisher | : John Wiley & Sons |
| Total Pages | : 418 |
| Release | : 2009-09-25 |
| Genre | : Mathematics |
| ISBN | : 0470317566 |
Written for the professional statistician or graduate statistics student, the primary objective of this book is to describe a system, based on the LISP language, for statistical computing and dynamic graphics to show how it can be used as an effective platform for a wide range of statistical computing tasks ranging from basic calculations to customizing dynamic graphs. In addition, it introduces object-oriented programming and graphics programming in a statistical context. The discussion of these ideas is based on the Lisp-Stat system; readers with access to such a system can reproduce the examples presented and use them as a basis for further experimentation and study.
| Author | : Randall L. Eubank |
| Publisher | : CRC Press |
| Total Pages | : 558 |
| Release | : 2011-12-01 |
| Genre | : Mathematics |
| ISBN | : 1420066501 |
With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a "boot-camp" on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.
| Author | : Francis John Anscombe |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 440 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461394503 |
A t the terminal seated, the answering tone: pond and temple bell. ODAY as in the past, statistical method is profoundly affected by T resources for numerical calculation and visual display. The main line of development of statistical methodology during the first half of this century was conditioned by, and attuned to, the mechanical desk calculator. Now statisticians may use electronic computers of various kinds in various modes, and the character of statistical science has changed accordingly. Some, but not all, modes of modern computation have a flexibility and immediacy reminiscent of the desk calculator. They preserve the virtues of the desk calculator, while immensely exceeding its scope. Prominent among these is the computer language and conversational computing system known by the initials APL. This book is addressed to statisticians. Its first aim is to interest them in using APL in their work-for statistical analysis of data, for numerical support of theoretical studies, for simulation of random processes. In Part A the language is described and illustrated with short examples of statistical calculations. Part B, presenting some more extended examples of statistical analysis of data, has also the further aim of suggesting the interplay of computing and theory that must surely henceforth be typical of the develop ment of statistical science.
