An Introduction to Bootstrap Methods with Applications to R
An Introduction to Bootstrap Methods with Applications to R

Author: Michael R. Chernick

Publisher: John Wiley & Sons

Published: 2014-08-21

Total Pages: 318

ISBN-13: 1118625412

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A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.

The Fundamentals of Heavy Tails
The Fundamentals of Heavy Tails

Author: Jayakrishnan Nair

Publisher: Cambridge University Press

Published: 2022-06-09

Total Pages: 266

ISBN-13: 1009062964

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Heavy tails –extreme events or values more common than expected –emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engaging package.

Achieving Nutrient and Sediment Reduction Goals in the Chesapeake Bay
Achieving Nutrient and Sediment Reduction Goals in the Chesapeake Bay

Author: National Research Council

Publisher: National Academies Press

Published: 2011-09-26

Total Pages: 258

ISBN-13: 0309210828

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The Chesapeake Bay is North America's largest and most biologically diverse estuary, as well as an important commercial and recreational resource. However, excessive amounts of nitrogen, phosphorus, and sediment from human activities and land development have disrupted the ecosystem, causing harmful algae blooms, degraded habitats, and diminished populations of many species of fish and shellfish. In 1983, the Chesapeake Bay Program (CBP) was established, based on a cooperative partnership among the U.S. Environmental Protection Agency (EPA), the state of Maryland, and the commonwealths of Pennsylvania and Virginia, and the District of Columbia, to address the extent, complexity, and sources of pollutants entering the Bay. In 2008, the CBP launched a series of initiatives to increase the transparency of the program and heighten its accountability and in 2009 an executive order injected new energy into the restoration. In addition, as part of the effect to improve the pace of progress and increase accountability in the Bay restoration, a two-year milestone strategy was introduced aimed at reducing overall pollution in the Bay by focusing on incremental, short-term commitments from each of the Bay jurisdictions. The National Research Council (NRC) established the Committee on the Evaluation of Chesapeake Bay Program Implementation for Nutrient Reduction in Improve Water Quality in 2009 in response to a request from the EPA. The committee was charged to assess the framework used by the states and the CBP for tracking nutrient and sediment control practices that are implemented in the Chesapeake Bay watershed and to evaluate the two-year milestone strategy. The committee was also to assess existing adaptive management strategies and to recommend improvements that could help CBP to meet its nutrient and sediment reduction goals. The committee did not attempt to identify every possible strategy that could be implemented but instead focused on approaches that are not being implemented to their full potential or that may have substantial, unrealized potential in the Bay watershed. Because many of these strategies have policy or societal implications that could not be fully evaluated by the committee, the strategies are not prioritized but are offered to encourage further consideration and exploration among the CBP partners and stakeholders.

All of Statistics
All of Statistics

Author: Larry Wasserman

Publisher: Springer Science & Business Media

Published: 2013-12-11

Total Pages: 446

ISBN-13: 0387217363

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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Probability and Statistics
Probability and Statistics

Author: Michael J. Evans

Publisher: Macmillan

Published: 2004

Total Pages: 704

ISBN-13: 9780716747420

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Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.

Generalized Additive Models
Generalized Additive Models

Author: Simon Wood

Publisher: CRC Press

Published: 2006-02-27

Total Pages: 412

ISBN-13: 1584884746

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Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.