Randomization Bootstrap And Monte Carlo Methods In Biology Third Edition
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Author |
: Bryan F.J. Manly |
Publisher |
: CRC Press |
Total Pages |
: 488 |
Release |
: 2006-08-15 |
ISBN-10 |
: 1584885416 |
ISBN-13 |
: 9781584885412 |
Rating |
: 4/5 (16 Downloads) |
Synopsis Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition by : Bryan F.J. Manly
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.
Author |
: Bryan F.J. Manly |
Publisher |
: CRC Press |
Total Pages |
: 428 |
Release |
: 1997-03-01 |
ISBN-10 |
: 0412721309 |
ISBN-13 |
: 9780412721304 |
Rating |
: 4/5 (09 Downloads) |
Synopsis Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition by : Bryan F.J. Manly
Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition features new material on on bootstrap confidence intervals and significance testing, and incorporates new developments on the treatments of randomization methods for regression and analysis variation, including descriptions of applications of these methods in spreadsheet programs such as Lotus and other commercial packages. This second edition illustrates the value of modern computer intensive methods in the solution of a wide range of problems, with particular emphasis on biological applications. Examples given in the text include the controversial topic of whether there is periodicity between co-occurrences of species on islands.
Author |
: Bryan F.J. Manly |
Publisher |
: CRC Press |
Total Pages |
: 468 |
Release |
: 2018-10-03 |
ISBN-10 |
: 9781482296419 |
ISBN-13 |
: 1482296411 |
Rating |
: 4/5 (19 Downloads) |
Synopsis Randomization, Bootstrap and Monte Carlo Methods in Biology by : Bryan F.J. Manly
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.
Author |
: Bryan F. J. Manly |
Publisher |
: Chapman & Hall/CRC |
Total Pages |
: 338 |
Release |
: 2022-04 |
ISBN-10 |
: 0367512874 |
ISBN-13 |
: 9780367512873 |
Rating |
: 4/5 (74 Downloads) |
Synopsis Randomization, Bootstrap, and Monte Carlo Methods in Biology by : Bryan F. J. Manly
The fourth edition of the book illustrates a large number of statistical methods with an emphasis on biological applications. It provides comprehensive coverage of computer-intensive applications, with datasets available online.
Author |
: Richard McElreath |
Publisher |
: CRC Press |
Total Pages |
: 489 |
Release |
: 2018-01-03 |
ISBN-10 |
: 9781482253481 |
ISBN-13 |
: 1482253488 |
Rating |
: 4/5 (81 Downloads) |
Synopsis Statistical Rethinking by : Richard McElreath
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Author |
: Sudipto Banerjee |
Publisher |
: CRC Press |
Total Pages |
: 586 |
Release |
: 2014-06-06 |
ISBN-10 |
: 9781420095388 |
ISBN-13 |
: 1420095382 |
Rating |
: 4/5 (88 Downloads) |
Synopsis Linear Algebra and Matrix Analysis for Statistics by : Sudipto Banerjee
Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra. The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear equations wherever possible. After illustrating the importance of the rank of a matrix, they discuss complementary subspaces, oblique projectors, orthogonality, orthogonal projections and projectors, and orthogonal reduction. The text then shows how the theoretical concepts developed are handy in analyzing solutions for linear systems. The authors also explain how determinants are useful for characterizing and deriving properties concerning matrices and linear systems. They then cover eigenvalues, eigenvectors, singular value decomposition, Jordan decomposition (including a proof), quadratic forms, and Kronecker and Hadamard products. The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces.
Author |
: Peihua Qiu |
Publisher |
: CRC Press |
Total Pages |
: 523 |
Release |
: 2013-10-14 |
ISBN-10 |
: 9781439847992 |
ISBN-13 |
: 1439847991 |
Rating |
: 4/5 (92 Downloads) |
Synopsis Introduction to Statistical Process Control by : Peihua Qiu
A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon the more established techniques. The author—a leading researcher on SPC—shows how these methods can handle new applications. After exploring the role of SPC and other statistical methods in quality control and management, the book covers basic statistical concepts and methods useful in SPC. It then systematically describes traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. The text also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring. All computations in the examples are solved using R, with R functions and datasets available for download on the author’s website. Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques.
Author |
: Andrew Gelman |
Publisher |
: CRC Press |
Total Pages |
: 663 |
Release |
: 2013-11-27 |
ISBN-10 |
: 9781439898208 |
ISBN-13 |
: 1439898200 |
Rating |
: 4/5 (08 Downloads) |
Synopsis Bayesian Data Analysis by : Andrew Gelman
Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied
Author |
: Thomas D. Cook |
Publisher |
: CRC Press |
Total Pages |
: 465 |
Release |
: 2007-11-19 |
ISBN-10 |
: 9781584880271 |
ISBN-13 |
: 1584880279 |
Rating |
: 4/5 (71 Downloads) |
Synopsis Introduction to Statistical Methods for Clinical Trials by : Thomas D. Cook
Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial. After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals. Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.
Author |
: Norman Matloff |
Publisher |
: CRC Press |
Total Pages |
: 490 |
Release |
: 2017-09-19 |
ISBN-10 |
: 9781498710923 |
ISBN-13 |
: 1498710921 |
Rating |
: 4/5 (23 Downloads) |
Synopsis Statistical Regression and Classification by : Norman Matloff
Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.