Download or read book Computer Intensive Methods in Statistics written by Silvelyn Zwanzig and published by CRC Press. This book was released on 2019-11-27 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. Computer Intensive Methods in Statistics is written for students at graduate level, but can also be used by practitioners. Features Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and illustrations for explaining the main ideas Features the theoretical backgrounds of the main methods. Includes R codes for the methods and examples Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics. Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods.
Download or read book Computer Intensive Statistical Methods written by J. S. Urban. Hjorth and published by Routledge. This book was released on 2017-10-19 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on computer intensive statistical methods, such as validation, model selection, and bootstrap, that help overcome obstacles that could not be previously solved by methods such as regression and time series modelling in the areas of economics, meteorology, and transportation.
Download or read book Statistical Methods in Water Resources written by D.R. Helsel and published by Elsevier. This book was released on 1993-03-03 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.
Download or read book Computer Intensive Methods for Testing Hypotheses written by Eric W. Noreen and published by Wiley-Interscience. This book was released on 1989-05-02 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods.
Download or read book Mathematica Laboratories for Mathematical Statistics written by Jenny A. Baglivo and published by SIAM. This book was released on 2005-01-01 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: CD-ROM contains text, data, computations, and graphics.
Download or read book Computer Intensive Methods in Statistics written by Wolfgang Härdle and published by Springer Science & Business Media. This book was released on 2013-11-27 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: The computer has created new fields in statistic. Numerical and statistical problems that were untackable five to ten years ago can now be computed even on portable personal computers. A computer intensive task is for example the numerical calculation of posterior distributions in Bayesian analysis. The Bootstrap and image analysis are two other fields spawned by the almost unlimited computing power. It is not only the computing power through that has revolutionized statistics, the graphical interactiveness on modern statistical environments has given us the possibility for deeper insight into our data. On November 21,22 1991 a conference on computer Intensive Methods in Statistics has been organized at the Universite Catholique de Louvain, Louvain-La-Neuve, Belgium. The organizers were Jan Beirlant (Katholieke Universiteit Leuven), Wolfgang Hardie (Humboldt-Universitat zu Berlin) and Leopold Simar (Universite Catholique de Louvain and Facultes Universitaires Saint-Louis). The meeting was the Xllth in the series of the Rencontre Franco-Beige des Statisticians. Following this tradition both theoretical statistical results and practical contributions of this active field of statistical research were presented. The four topics that have been treated in more detail were: Bayesian Computing; Interfacing Statistics and Computers; Image Analysis; Resampling Methods. Selected and refereed papers have been edited and collected for this book. 1) Bayesian Computing.
Download or read book Elements of Computational Statistics written by James E. Gentle and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books
Download or read book Randomization Bootstrap and Monte Carlo Methods in Biology written by Bryan F.J. Manly and published by CRC Press. This book was released on 2020-07-21 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications, with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods Makes it easy for biologists, researchers, and students to understand the methods used Provides information about computer programs and packages to implement calculations, particularly using R code Includes a large number of real examples from a range of biological disciplines Written in an accessible style, with minimal coverage of theoretical details, this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students, as well as a reference for researchers from a range of disciplines. The detailed, worked examples of real applications will enable practitioners to apply the methods to their own biological data.
Download or read book Elements of Statistical Computing written by R.A. Thisted and published by Routledge. This book was released on 2017-10-19 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Computational Statistics written by James E. Gentle and published by Springer Science & Business Media. This book was released on 2009-07-28 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods.
Download or read book Complex Models and Computational Methods in Statistics written by Matteo Grigoletto and published by Springer Science & Business Media. This book was released on 2013-01-26 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
Download or read book Probability and Statistical Inference written by Robert Bartoszynski and published by John Wiley & Sons. This book was released on 2007-11-16 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now updated in a valuable new edition—this user-friendly book focuses on understanding the "why" of mathematical statistics Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstanding new edition continues to encouragereaders to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics. Clear explanations are presented and appliedto various examples that help to impart a deeper understanding of theorems and methods—from fundamental statistical concepts to computational details. Additional features of this Second Edition include: A new chapter on random samples Coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book's FTP site Treatment of survival and hazard function, methods of obtaining estimators, and Bayes estimating Real-world examples that illuminate presented concepts Exercises at the end of each section Providing a straightforward, contemporary approach to modern-day statistical applications, Probability and Statistical Inference, Second Edition is an ideal text for advanced undergraduate- and graduate-level courses in probability and statistical inference. It also serves as a valuable reference for practitioners in any discipline who wish to gain further insight into the latest statistical tools.
Download or read book An Introduction to Statistical Computing written by Jochen Voss and published by John Wiley & Sons. This book was released on 2013-08-28 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introduction to Computer Intensive Methods of Data Analysis in Biology written by Derek A. Roff and published by Cambridge University Press. This book was released on 2006-05-25 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Download or read book Statistical Computing with R written by Maria L. Rizzo and published by CRC Press. This book was released on 2007-11-15 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Randomization Bootstrap and Monte Carlo Methods in Biology written by Bryan F.J. Manly and published by CRC Press. This book was released on 2018-10-03 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis on biological applications. This textbook focuses on three related areas in computational statistics: randomization, bootstrapping, and Monte Carlo methods of inference. The author emphasizes the sampling approach within randomization testing and confidence intervals. Similar to randomization, the book shows how bootstrapping, or resampling, can be used for confidence intervals and tests of significance. It also explores how to use Monte Carlo methods to test hypotheses and construct confidence intervals. New to the Third Edition Updated information on regression and time series analysis, multivariate methods, survival and growth data as well as software for computational statistics References that reflect recent developments in methodology and computing techniques Additional references on new applications of computer-intensive methods in biology Providing comprehensive coverage of computer-intensive applications while also offering data sets online, Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition supplies a solid foundation for the ever-expanding field of statistics and quantitative analysis in biology.
Download or read book Bootstrapping written by Christopher Z. Mooney and published by SAGE. This book was released on 1993-08-09 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.
