23 results (0,18104 seconds)

Show all filters

Brand

Merchant

Price (EUR)

Reset filter

Products
From
Shops

Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests: A Computational Approach describes classical nonparametric tests as well as novel and little-known methods such as the Baumgartner-Weiss-Schindler and the Cucconi tests. The book presents SAS and R programs allowing readers to carry out the different statistical methods such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods. Numerous real-life data from various areas including the bible and their analyses provide for greatly diversified reading. The book covers: Nonparametric two-sample tests for the location-shift model specifically the Fisher-Pitman permutation test the Wilcoxon rank sum test and the Baumgartner-Weiss-Schindler test Permutation tests location-scale tests tests for the nonparametric Behrens-Fisher problem and tests for a difference in variability Tests for the general alternative including the (Kolmogorov-)Smirnov test ordered categorical and discrete numerical data Well-known one-sample tests such as the sign test and Wilcoxon’s signed rank test a modification suggested by Pratt (1959) a permutation test with original observations and a one-sample bootstrap test are presented. Tests for more than two groups the following tests are described in detail: the Kruskal-Wallis test the permutation F test the Jonckheere-Terpstra trend test tests for umbrella alternatives and the Friedman and Page tests for multiple dependent groups The concepts of independence and correlation and stratified tests such as the van Elteren test and combination tests The applicability of computer-intensive methods such as bootstrap and permutation tests for non-standard situations and complex designs Although the major development of nonparametric methods came to a certain end in the 1970s their importance undoubtedly persists. What is still needed is a computer assisted evaluation of their main properties. This book closes that gap. | Nonparametric Statistical Tests A Computational Approach

GBP 69.99
1

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

In engineering and quality control various situations including process validation and design verification require equivalence and noninferiority tests. Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers presents methods for using validation and verification test data to demonstrate equivalence and noninferiority in engineering and applied science. The book covers numerous tests drawn from the author’s more than 30 years of work in a range of industrial settings. It provides computational formulas for the tests methods to determine or justify sample sizes and formulas to calculate power and operating characteristic curves. The methods are accessible using standard statistical software and do not require complicated programming. The book also includes computer code and screen shots for SAS R and JMP. This book provides you with a guide to performing validation and verification tests that demonstrate the adequacy of your process system or product. It will help you choose the best test for your application.

GBP 59.99
1

Basic Statistics and Pharmaceutical Statistical Applications

Basic Statistics and Pharmaceutical Statistical Applications

Building on its best-selling predecessors Basic Statistics and Pharmaceutical Statistical Applications Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter this third edition now provides information on software applications to assist with evaluating data. New to the Third EditionUse of Excel® and Minitab® for performing statistical analysisDiscussions of nonprobability sampling procedures determining if data is normally distributed evaluation of covariances and testing for precision equivalenceExpanded sections on regression analysis chi square tests tests for trends with ordinal data and tests related to survival statistics Additional nonparametric procedures including the one-sided sign test Wilcoxon signed-ranks test and Mood’s median testWith the help of flow charts and tables the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps readers correctly interpret their results using statistical software. Through the text’s worked-out examples readers better understand how the mathematics works the logic behind many of the equations and the tests’ outcomes.

GBP 44.99
1

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented but robust techniques are considered as well. These techniques include one-sample testing and estimation multi-sample testing and estimation and regression. Attention is paid to the intellectual development of the field with a thorough review of bibliographical references. Computational tools in R and SAS are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign Kruskal-Wallis Friedman Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology elementary probability and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration and ideally a course in matrix algebra.

GBP 84.99
1

Statistical Thinking in Clinical Trials

Statistical Thinking in Clinical Trials

Statistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ’ principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V’ principle provides a simple estimator of the log odds ratio that is ideally suited for stratified analysis with a binary outcome. The same principle can be used to estimate the log hazard ratio and facilitate stratified analysis in a survival setting. Learn these and other simple techniques that will make you an invaluable clinical trial statistician.

GBP 82.99
1

Handbook of Statistical Distributions with Applications

Handbook of Statistical Distributions with Applications

Easy-to-Use Reference and Software for Statistical Modeling and TestingHandbook of Statistical Distributions with Applications Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations. Along with many new examples and results this edition includes both the author’s StatCalc software and R codes to accurately and easily carry out computations. New to the Second Edition Major changes in binomial Poisson normal gamma Weibull exponential logistic Laplace and Pareto distributions Updated statistical tests and intervals based on recent publications in statistical journals Enhanced PC calculator StatCalc with electronic help manualsR functions for cases where StatCalc is not applicable with the codes available onlineThis highly praised handbook integrates popular probability distribution models formulas applications and software to help you compute a variety of statistical intervals. It covers probability and percentiles algorithms for random number generation hypothesis tests confidence intervals tolerance intervals prediction intervals sample size determination and much more.

GBP 44.99
1

Analytical Similarity Assessment in Biosimilar Product Development

Analytical Similarity Assessment in Biosimilar Product Development

This book focuses on analytical similarity assessment in biosimilar product development following the FDA’s recommended stepwise approach for obtaining totality-of-the-evidence for approval of biosimilar products. It covers concepts such as the tiered approach for assessment of similarity of critical quality attributes in the manufacturing process of biosimilar products models/methods like the statistical model for classification of critical quality attributes equivalence tests for critical quality attributes in Tier 1 and the corresponding sample size requirements current issues and recent developments in analytical similarity assessment.

GBP 48.99
1

Real Analysis and Foundations

Real Analysis and Foundations

Through four editions this popular textbook attracted a loyal readership and widespread use. Students find the book to be concise accessible and complete. Instructors find the book to be clear authoritative and dependable. The primary goal of this new edition remains the same as in previous editions. It is to make real analysis relevant and accessible to a broad audience of students with diverse backgrounds while also maintaining the integrity of the course. This text aims to be the generational touchstone for the subject and the go-to text for developing young scientists. This new edition continues the effort to make the book accessible to a broader audience. Many students who take a real analysis course do not have the ideal background. The new edition offers chapters on background material like set theory logic and methods of proof. The more advanced material in the book is made more apparent. This new edition offers a new chapter on metric spaces and their applications. Metric spaces are important in many parts of the mathematical sciences including data mining web searching and classification of images. The author also revised the material on sequences and series adding examples and exercises that compare convergence tests and give additional tests. The text includes rare topics such as wavelets and applications to differential equations. The level of difficulty moves slowly becoming more sophisticated in later chapters. Students have commented on the progression as a favorite aspect of the textbook. The author is perhaps the most prolific expositor of upper division mathematics. With over seventy books in print thousands of students have been taught and learned from his books. | Real Analysis and Foundations

GBP 82.99
1

Essentials of Probability Theory for Statisticians

Essentials of Probability Theory for Statisticians

Essentials of Probability Theory for Statisticians provides graduate students with a rigorous treatment of probability theory with an emphasis on results central to theoretical statistics. It presents classical probability theory motivated with illustrative examples in biostatistics such as outlier tests monitoring clinical trials and using adaptive methods to make design changes based on accumulating data. The authors explain different methods of proofs and show how they are useful for establishing classic probability results. After building a foundation in probability the text intersperses examples that make seemingly esoteric mathematical constructs more intuitive. These examples elucidate essential elements in definitions and conditions in theorems. In addition counterexamples further clarify nuances in meaning and expose common fallacies in logic. This text encourages students in statistics and biostatistics to think carefully about probability. It gives them the rigorous foundation necessary to provide valid proofs and avoid paradoxes and nonsensical conclusions.

GBP 56.99
1

C++ for Financial Mathematics

C++ for Financial Mathematics

If you know a little bit about financial mathematics but don’t yet know a lot about programming then C++ for Financial Mathematics is for you. C++ is an essential skill for many jobs in quantitative finance but learning it can be a daunting prospect. This book gathers together everything you need to know to price derivatives in C++ without unnecessary complexities or technicalities. It leads the reader step-by-step from programming novice to writing a sophisticated and flexible financial mathematics library. At every step each new idea is motivated and illustrated with concrete financial examples. As employers understand there is more to programming than knowing a computer language. As well as covering the core language features of C++ this book teaches the skills needed to write truly high quality software. These include topics such as unit tests debugging design patterns and data structures. The book teaches everything you need to know to solve realistic financial problems in C++. It can be used for self-study or as a textbook for an advanced undergraduate or master’s level course.

GBP 44.99
1

The Weibull Distribution A Handbook

The Weibull Distribution A Handbook

The Most Comprehensive Book on the SubjectChronicles the Development of the Weibull Distribution in Statistical Theory and Applied StatisticsExploring one of the most important distributions in statistics The Weibull Distribution: A Handbook focuses on its origin statistical properties and related distributions. The book also presents various approaches to estimate the parameters of the Weibull distribution under all possible situations of sampling data as well as approaches to parameter and goodness-of-fit testing. Describes the Statistical Methods Concepts Theories and Applications of This DistributionCompiling findings from dozens of scientific journals and hundreds of research papers the author first gives a careful and thorough mathematical description of the Weibull distribution and all of its features. He then deals with Weibull analysis using classical and Bayesian approaches along with graphical and linear maximum likelihood techniques to estimate the three Weibull parameters. The author also explores the inference of Weibull processes Weibull parameter testing and different types of goodness-of-fit tests and methods. Successfully Apply the Weibull ModelBy using inferential procedures for estimating testing forecasting and simulating data this self-contained detailed handbook shows how to solve statistical life science and engineering problems. | The Weibull Distribution A Handbook

GBP 44.99
1

A Course in the Large Sample Theory of Statistical Inference

A Course in the Large Sample Theory of Statistical Inference

This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. Asymptotic results for estimation and testing are derived using the “moving alternative” formulation due to R. A. Fisher and L. Le Cam. Later chapters include discussions of linear rank statistics and of chi-squared tests for contingency table analysis including situations where parameters are estimated from the complete ungrouped data. This book is based on lecture notes prepared by the first author subsequently edited expanded and updated by the second author. Key features: Succinct account of the concept of “asymptotic linearity” and its uses Simplified derivations of the major results under an assumption of joint asymptotic normality Inclusion of numerical illustrations practical examples and advice Highlighting some unexpected consequences of the theory Large number of exercises many with hints to solutions Some facility with linear algebra and with real analysis including ‘epsilon-delta’ arguments is required. Concepts and results from measure theory are explained when used. Familiarity with undergraduate probability and statistics including basic concepts of estimation and hypothesis testing is necessary and experience with applying these concepts to data analysis would be very helpful. | A Course in the Large Sample Theory of Statistical Inference

GBP 84.99
1

Introductory Statistical Inference

Introductory Statistical Inference

This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial using worked examples exercises figures tables and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques. Beginning with a review of the basic concepts and methods in probability theory moments and moment generating functions the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables exponential families of distributions and standard probability inequalities. It develops the Helmert transformation for normal distributions introduces the notions of convergence and spotlights the central limit theorems. Coverage highlights sampling distributions Basu's theorem Rao-Blackwellization and the Cramér-Rao inequality. The text also provides in-depth coverage of Lehmann-Scheffé theorems focuses on tests of hypotheses describes Bayesian methods and the Bayes' estimator and develops large-sample inference. The author provides a historical context for statistics and statistical discoveries and answers to a majority of the end-of-chapter exercises. Designed primarily for a one-semester first-year graduate course in probability and statistical inference this text serves readers from varied backgrounds ranging from engineering economics agriculture and bioscience to finance financial mathematics operations and information management and psychology.

GBP 59.99
1

Randomization Bootstrap and Monte Carlo Methods in Biology

Randomization Bootstrap and Monte Carlo Methods in Biology

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.

GBP 44.99
1

Probability Statistics and Data A Fresh Approach Using R

Probability Statistics and Data A Fresh Approach Using R

This book is a fresh approach to a calculus based first course in probability and statistics using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included using calculus when appropriate but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized quantified using simulations and demonstrated with real data. The book compares parametric and non-parametric methods through simulation allowing for a thorough investigation of testing error and power. The text builds R skills from the outset allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers so that you are working with current real data which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr ggplot2 tidyr stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University and the majority of the more than 400 exercises have been classroom tested. | Probability Statistics and Data A Fresh Approach Using R

GBP 82.99
1

Designing Network On-Chip Architectures in the Nanoscale Era

Designing Network On-Chip Architectures in the Nanoscale Era

Going beyond isolated research ideas and design experiences Designing Network On-Chip Architectures in the Nanoscale Era covers the foundations and design methods of network on-chip (NoC) technology. The contributors draw on their own lessons learned to provide strong practical guidance on various design issues. Exploring the design process of the network the first part of the book focuses on basic aspects of switch architecture and design topology selection and routing implementation. In the second part contributors discuss their experiences in the industry offering a roadmap to recent products. They describe Tilera’s TILE family of multicore processors novel Intel products and research prototypes and the TRIPS operand network (OPN). The last part reveals state-of-the-art solutions to hardware-related issues and explains how to efficiently implement the programming model at the network interface. In the appendix the microarchitectural details of two switch architectures targeting multiprocessor system-on-chips (MPSoCs) and chip multiprocessors (CMPs) can be used as an experimental platform for running tests. A stepping stone to the evolution of future chip architectures this volume provides a how-to guide for designers of current NoCs as well as designers involved with 2015 computing platforms. It cohesively brings together fundamental design issues alternative design paradigms and techniques and the main design tradeoffs—consistently focusing on topics most pertinent to real-world NoC designers.

GBP 59.99
1

Principles of Biostatistics

Principles of Biostatistics

Principles of Biostatistics Third Edition is a concepts-based introduction to statistical procedures that prepares public health medical and life sciences students to conduct and evaluate research. With an engaging writing style and helpful graphics the emphasis is on concepts over formulas or rote memorization. Throughout the book the authors use practical interesting examples with real data to bring the material to life. Thoroughly revised and updated this third edition includes a new chapter introducing the basic principles of Study Design as well as new sections on sample size calculations for two-sample tests on means and proportions the Kruskal-Wallis test and the Cox proportional hazards model. Key Features: Includes a new chapter on the basic principles of study design. Additional review exercises have been added to each chapter. Datasets and Stata and R code are available on the book’s website. The book is divided into three parts. The first five chapters deal with collections of numbers and ways in which to summarize explore and explain them. The next two chapters focus on probability and introduce the tools needed for the subsequent investigation of uncertainty. It is only in the eighth chapter and thereafter that the authors distinguish between populations and samples and begin to investigate the inherent variability introduced by sampling thus progressing to inference. Postponing the slightly more difficult concepts until a solid foundation has been established makes it easier for the reader to comprehend them.

GBP 74.99
1

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry academia and government. It introduces the tidyverse suite of R packages including the ggplot2 package for data visualization and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses the book covers traditional introductory statistics topics like confidence intervals hypothesis testing and multiple regression modeling while focusing on visualization throughout. Features: ● Assumes minimal prerequisites notably no prior calculus nor coding experience ● Motivates theory using real-world data including all domestic flights leaving New York City in 2013 the Gapminder project and the data journalism website FiveThirtyEight. com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for tidy and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive. com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics at both the undergraduate and graduate levels.

GBP 66.99
1

Logistic Regression Models

Logistic Regression Models

Logistic Regression Models presents an overview of the full range of logistic models including binary proportional ordered partially ordered and unordered categorical response regression procedures. Other topics discussed include panel survey skewed penalized and exact logistic models. The text illustrates how to apply the various models to health environmental physical and social science data. Examples illustrate successful modelingThe text first provides basic terminology and concepts before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression varieties of overdispersion and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. Apply the models to your own dataData files for examples and questions used in the text as well as code for user-authored commands are provided on the book’s website formatted in Stata R Excel SAS SPSS and Limdep. See Professor Hilbe discuss the book.

GBP 52.99
1

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem social and infrastructure resilience given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation climate modeling and long-term prediction; approach the problems of increasing frequency of extreme events sea level rise and forest fires as well as economic losses analysis of climate impacts for insurance agriculture fisheries and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling statistics and machine learning including the global circulation models (GCM) and ocean models statistical generalized additive models (GAM) and generalized linear models (GLM) state space and graphical models causality networks Bayesian ensembles a variety of index methods and statistical tests and machine learning methods. The reader will learn about data from various sources including GCM and ocean model outputs satellite observations and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

GBP 54.99
1

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Reviewing the theory of the general linear model (GLM) using a general framework Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition presents analyses of simple and complex models both univariate and multivariate that employ data sets from a variety of disciplines such as the social and behavioral sciences. With revised examples that include options available using SAS 9. 0 this expanded edition divides theory from applications within each chapter. Following an overview of the GLM the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis and multivariate GLMs that cover multivariate regression analysis MANOVA MANCOVA and repeated measurement data analyses. The book also analyzes double multivariate linear growth curve seeming unrelated regression (SUR) restricted GMANOVA and hierarchical linear models. New to the Second EditionTwo chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedureExpanded theory of unrestricted general linear multivariate general linear SUR and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithmApplications of MI MIANALYZE TRANSREG and CALIS proceduresA practical introduction to GLMs Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework. | Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

GBP 56.99
1

Statistical Simulation Power Method Polynomials and Other Transformations

Statistical Simulation Power Method Polynomials and Other Transformations

Although power method polynomials based on the standard normal distributions have been used in many different contexts for the past 30 years it was not until recently that the probability density function (pdf) and cumulative distribution function (cdf) were derived and made available. Focusing on both univariate and multivariate nonnormal data generation Statistical Simulation: Power Method Polynomials and Other Transformations presents techniques for conducting a Monte Carlo simulation study. It shows how to use power method polynomials for simulating univariate and multivariate nonnormal distributions with specified cumulants and correlation matrices. The book first explores the methodology underlying the power method before demonstrating this method through examples of standard normal logistic and uniform power method pdfs. It also discusses methods for improving the performance of a simulation based on power method polynomials. The book then develops simulation procedures for systems of linear statistical models intraclass correlation coefficients and correlated continuous variates and ranks. Numerical examples and results from Monte Carlo simulations illustrate these procedures. The final chapter describes how the g-and-h and generalized lambda distribution (GLD) transformations are special applications of the more general multivariate nonnormal data generation approach. Throughout the text the author employs Mathematica® in a range of procedures and offers the source code for download online. Written by a longtime researcher of the power method this book explains how to simulate nonnormal distributions via easy-to-use power method polynomials. By using the methodology and techniques developed in the text readers can evaluate different transformations in terms of comparing percentiles measures of central tendency goodness-of-fit tests and more. | Statistical Simulation Power Method Polynomials and Other Transformations

GBP 64.99
1

Statistical Computing with R Second Edition

Statistical Computing with R Second Edition

Praise for the First Edition: . the book serves as an excellent tutorial on the R language providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation. – Tzvetan Semerdjiev Zentralblatt Math Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational graphical and numerical approaches to solving statistical problems. Like its bestselling predecessor Statistical Computing with R Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of computational statistics and an introduction to the R computing environment. Focuses on implementation rather than theory. Explores key topics in statistical computing including Monte Carlo methods in inference bootstrap and jackknife permutation tests Markov chain Monte Carlo (MCMC) methods and density estimation. Includes new sections exercises and applications as well as new chapters on resampling methods and programming topics. Includes coverage of recent advances including R Studio the tidyverse knitr and ggplot2 Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics. Suitable for an introductory course in computational statistics or for self-study Statistical Computing with R Second Edition provides a balanced accessible introduction to computational statistics and statistical computing. About the Author Maria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green Ohio where she teaches statistics actuarial science computational statistics statistical programming and data science. Prior to joining the faculty at BGSU in 2006 she was Assistant Professor in the Department of Mathematics at Ohio University in Athens Ohio. Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics.

GBP 66.99
1