This tells the story of Douglas Engelbart's revolutionary vision, reaching beyond conventional histories of Silicon Valley to probe the ideology that shaped some of the basic ingredients of contemporary life.
"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.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Greg Gianforte, the nation's top Bootstrapper, shows you the advantages of Bootstrapping vs. traditionally financed start-ups. You'll also learn how the unconventional Bootstrapping mindset-inventive, pioneering, and skeptical of conventional wisdom-applies to you and your business. With Bootstrapping Your Business at your side, you'll gain the advantage you need to outperform the competition-and succeed in today's take-no-prisoners marketplace.
Bootstrapping is a conceptually simple statistical technique to increase the quality of estimates, conduct robustness checks and compute standard errors for virtually any statistic. This book provides an intelligible and compact introduction for students, scientists and practitioners. It not only gives a clear explanation of the underlying concepts but also demonstrates the application of bootstrapping using Python and Stata.
Strategic Bootstrapping is about helping entrepreneurs sift through the “noise” regarding bootstrapping a start-up. The cold-hard facts on bootstrapping are presented. Practically speaking, most entrepreneurs should avoid bootstrapping, realistically, most entrepreneurs will need to engage in some form of bootstrapping. The argument then shifts to how should one bootstrap? In this era of lean start-ups, effectuation, and bricolage, bootstrapping is oft romanticized but seldom analyzed. This book is different from other bootstrapping books in two key ways. First, it draws on evidence from scienti c study to offer best practices. Second, it utilizes this evidence to help en- trepreneurs thrive—not just survive.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
This monograph addresses two quite different topics, each being able to shed light on the other. Firstly, it lays the foundation for a particular view of the bootstrap. Secondly, it gives an account of Edgeworth expansion. The first two chapters deal with the bootstrap and Edgeworth expansion respectively, while chapters 3 and 4 bring these two themes together, using Edgeworth expansion to explore and develop the properties of the bootstrap. The book is aimed at graduate level for those with some exposure to the methods of theoretical statistics. However, technical details are delayed until the last chapter such that mathematically able readers without knowledge of the rigorous theory of probability will have no trouble understanding most of the book.