Approaches To Bootstrapping

Volume 1 of Approaches to Bootstrapping focuses on early word learning and syntactic development with special emphasis on the bootstrapping mechanisms by which the child using properties of the speech input enters the native linguistic system. Topics discussed in the area of lexical acquisition are: cues and mechanisms for isolating words in the input; special features of motherese and their role for early word learning; the determination of first word meanings; memory and related processing capacities in early word learning and understanding; and lexical representation and lexical access in early language production. The papers on syntactic development deal with the acquisition of grammatical prosodic features for learning language specific syntactic regularities.Volume 2 of Approaches to Bootstrapping focuses on the interaction between the development of prosodic and morphosyntactic knowledge as evidenced in the early speech of Dutch, English, German, Portugese, Spanish, Danish, Islandic, and Swedish children sheding new light on the relation between universal and language specific aspects of language acquisition. Another section of this volume deals with new approaches to language acquisition using ERP- techniques. The papers discuss in detail the relation between the development of language skills and changes in neurophysiological aspects of the brain. The potentials of these techniques for the development of new tools for an early diagnosis of children who are at risque for developmental language disorders are also pointed out. The closing section contains a synopsis of interactionist approaches to language acquisition, a discussion of the genetic and experiential origin of primitive linguistic elements in acquisition, and a discussion of structural and developmental aspects of bird song in comparison to human language. The two volumes making up Approaches to Bootstrapping present a state-of-the art interdisciplinary and cross-linguistic overview of recent developments in first language acquisition research.

"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.

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.

A practical and accessible introduction to the bootstrap method——newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features: The addition of a second, extended bibliography devoted solely to publications from 1999–2007, which is a valuable collection of references on the latest research in the field A discussion of the new areas of applicability for bootstrap methods, including use in the pharmaceutical industry for estimating individual and population bioequivalence in clinical trials A revised chapter on when and why bootstrap fails and remedies for overcoming these drawbacks Added coverage on regression, censored data applications, P-value adjustment, ratio estimators, and missing data New examples and illustrations as well as extensive historical notes at the end of each chapter With a strong focus on application, detailed explanations of methodology, and complete coverage of modern developments in the field, Bootstrap Methods, Second Edition is an indispensable reference for applied statisticians, engineers, scientists, clinicians, and other practitioners who regularly use statistical methods in research. It is also suitable as a supplementary text for courses in statistics and resampling methods at the upper-undergraduate and graduate levels.

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.

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.

Disk contains the library functions and documentation for use with Splus for Windows.