Bootstrap methods are computer-intensive methods of statistical analysis, which use simulation to calculate standard errors, confidence intervals, and significance tests. The methods apply for any level of modelling, and so can be used for fully parametric, semiparametric, and completely nonparametric analysis. This 1997 book gives a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis. Applications include stratified data; finite populations; censored and missing data; linear, nonlinear, and smooth regression models; classification; time series and spatial problems. Special features of the book include: extensive discussion of significance tests and confidence intervals; material on various diagnostic methods; and methods for efficient computation, including improved Monte Carlo simulation. Each chapter includes both practical and theoretical exercises. S-Plus programs for implementing the
Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.
Agricultural historians have collected and published a remarkable amount of material in recent years, partly as a result of the ongoing series 'The Agrarian History of England and Wales'. Missing from the Agrarian History volumes covering 1640–1850 has been any sustained analysis of agricultural rent, a perhaps surprising omission in view of the enormous sums of money which passed between landlords and tenants annually, and given the importance of the subject in terms of our understanding of the general course of change in agriculture and the economy more generally. In recent years the availability of estate accounts in public archive repositories has made available a range of data for the period c.1690 to the First World War, after which the material is voluminous and well known. In this book, based on research in archives across the country, the authors have produced a new rent index which will become the basis on which all future researchers in the field will rely.
The question of why organisms reproduce sexually is still a matter of controversy. In this account, Professor Maynard Smith considers the selective forces responsible for the origin and evolution of sexual reproduction and genetic recombination, using quantitative population genetics arguments to support his ideas. The relative importance of individual and group selection processes are also considered. the aim is to give a clear statement of the theoretical issues, and present enough of the evidence to show what kinds of facts are relevant. It is hoped that where crucial evidence is missing, experimentalists and field workers may be encouraged to collect the relevant data. The author does not claim to solve all the problems he raises, but this clear and well-argued account should provide stimulating reading for advanced undergraduate students and research workers in evolutionary theory.
At first glance, studying behavior is easy, but as every budding ethologist quickly realises, there are a host of complex practical, methodological and analytical problems to solve before designing and conducting the study. How do you choose which species or which behavior to study? What equipment will you need to observe and record behavior successfully? How do you record data in the dark, in the wet, or without missing part of the action? How do you analyse and interpret the data to yield meaningful information? This new expanded edition of the Handbook of Ethological Methods provides a complete step-by-step introduction to ethological methods from topic choice and behavioral description to data collection and statistical analysis. This book will be a must for beginning students and experienced researchers studying animal behavior in the field or laboratory.
Patient reported outcome measures are central to the evaluation of medical care and treatment regimes. Such measures depart from traditional clinical assessments as they are based on issues known to be of importance to patients. This book outlines the development and application of a variety of such measures in a wide range of neurological conditions. Introductory chapters outline issues in the application and validation of quality-of-life measures in neurology. Subsequent chapters survey the most widely used quality-of-life instruments in Parkinson's disease, motor neurone disease, multiple sclerosis, multiple system atrophy, progressive supranuclear palsy, and Alzheimer's/dementia. A chapter on cerebral palsy deals with the particular challenges to developing outcome measures for children. The book also addresses issues relating to the translation of measures for use in cross-cultural studies, handling missing data, carer experiences of long-term conditions, and methodological challe
During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book con
DNA ancestry companies generate revenues in the region of $1bn a year, and the company 23andMe is said to have sold 10 million DNA ancestry kits to date. Although evidently popular, the science behind how DNA ancestry tests work is mystifying and difficult for the general public to interpret and understand. In this accessible and engaging book, Sheldon Krimsky, a leading researcher, investigates the methods that different companies use for DNA ancestry testing. He also discusses what the tests are used for, from their application in criminal investigations to discovering missing relatives. With a lack of transparency from companies in sharing their data, absent validation of methods by independent scientists, and currently no agreed-upon standards of accuracy, this book also examines the ethical issues behind genetic genealogy testing, including concerns surrounding data privacy and security. It demystifies the art and science of DNA ancestry testing for the general reader.
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinic
This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinic
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title,
Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical r
Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with Wil
Paradigmatic gaps ('missing' inflected forms) have traditionally been considered to be the random detritus of a language's history and marginal exceptions to the normal functioning of its inflectional system. Arguing that this is a misperception, Inflectional Defectiveness demonstrates that paradigmatic gaps are in fact normal and expected products of inflectional structure. Sims offers an accessible exploration of how and why inflectional defectiveness arises, why it persists, and how it is learned. The book presents a theory of morphology which is rooted in the implicative structure of the paradigm. This systematic exploration of the topic also addresses questions of inflection class organization, the morphology-syntax interface, the structure of the lexicon, and the nature of productivity. Presenting a novel synthesis of established research and new empirical data, this work is significant for researchers and graduate students in all fields of linguistics.
The authors use hard data and science to debunk a number of common misconceptions surrounding missing children, cell phones, breast implants, AIDS, cancer, radiation, vitamins, and violent crime rates
Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title,
At first glance, studying behavior is easy, but as every budding ethologist quickly realises, there are a host of complex practical, methodological and analytical problems to solve before designing and conducting the study. How do you choose which species or which behavior to study? What equipment will you need to observe and record behavior successfully? How do you record data in the dark, in the wet, or without missing part of the action? How do you analyse and interpret the data to yield meaningful information? This new expanded edition of the Handbook of Ethological Methods provides a complete step-by-step introduction to ethological methods from topic choice and behavioral description to data collection and statistical analysis. This book will be a must for beginning students and experienced researchers studying animal behavior in the field or laboratory.
Paradigmatic gaps ('missing' inflected forms) have traditionally been considered to be the random detritus of a language's history and marginal exceptions to the normal functioning of its inflectional system. Arguing that this is a misperception, Inflectional Defectiveness demonstrates that paradigmatic gaps are in fact normal and expected products of inflectional structure. Sims offers an accessible exploration of how and why inflectional defectiveness arises, why it persists, and how it is learned. The book presents a theory of morphology which is rooted in the implicative structure of the paradigm. This systematic exploration of the topic also addresses questions of inflection class organization, the morphology-syntax interface, the structure of the lexicon, and the nature of productivity. Presenting a novel synthesis of established research and new empirical data, this work is significant for researchers and graduate students in all fields of linguistics.
