The first edition of this book (1970) set out a systematic basis for the analysis of binary data and in particular for the study of how the probability of 'success' depends on explanatory variables. The first edition has been widely used and the general level and style have been preserved in the second edition, which contains a substantial amount of new material. This amplifies matters dealt with only cryptically in the first edition and includes many more recent developments. In addition the whole material has been reorganized, in particular to put more emphasis on m.aximum likelihood methods. There are nearly 60 further results and exercises. The main points are illustrated by practical examples, many of them not in the first edition, and some general essential background material is set out in new Appendices.
Providing reliable information on an intervention effect, meta-analysis is a powerful statistical tool for analyzing and combining results from individual studies. Meta-Analysis of Binary Data Using Profile Likelihood focuses on the analysis and modeling of a meta-analysis with individually pooled data (MAIPD). It presents a unifying approac
A textbook of an intermediate level, this work shows how binary data can be analyzed using a modelling approach, dwelling on practical aspects, incorporating recent work on checking the adequacy of fitted models and showing how computational facilities can be exploited.
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking. It includes contributions on analyzing and interpreting large, complex and aggregated datasets, and highlights numerous applications in economics, finance, computer science, political science and education. It gathers a selection of peer-reviewed contributions presented at the 16th Conference of the International Federation of Classification Societies (IFCS 2019), which was organized by the Greek Society of Data Analysis and held in Thessaloniki, Greece, on August 26-29, 2019.
Uncover the secrets of Linux binary analysis with this handy guide About This Book Grasp the intricacies of the ELF binary format of UNIX and Linux Design tools for reverse engineering and binary forensic analysis Insights into UNIX and Linux memory infections, ELF viruses, and binary protection schemes Who This Book Is For If you are a software engineer or reverse engineer and want to learn more about Linux binary analysis, this book will provide you with all you need to implement solutions for binary analysis in areas of security, forensics, and antivirus. This book is great for both security enthusiasts and system level engineers. Some experience with the C programming language and the Linux command line is assumed. What You Will Learn Explore the internal workings of the ELF binary format Discover techniques for UNIX Virus infection and analysis Work with binary hardening and software anti-tamper methods Patch executables and process memory Bypass anti-debugging measures used in malware Perform advanced forensic analysis of binaries Design ELF-related tools in the C language Learn to operate on memory with ptrace In Detail Learning Linux Binary Analysis is packed with knowledge and code that will teach you the inner workings of the ELF format, and the methods used by hackers and security analysts for virus analysis, binary patching, software protection and more. This book will start by taking you through UNIX/Linux object utilities, and will move on to teaching you all about the ELF specimen. You will learn about process tracing, and will explore the different types of Linux and UNIX viruses, and how you can make use of ELF Virus Technology to deal with them. The latter half of the book discusses the usage of Kprobe instrumentation for kernel hacking, code patching, and debugging. You will discover how to detect and disinfect kernel-mode rootkits, and move on to analyze static code. Finally, you will be walked through complex userspace memory infection analysis. This book will lead you into territory that is uncharted even by some experts; right into the world of the computer hacker. Style and approach The material in this book provides detailed insight into the arcane arts of hacking, coding, reverse engineering Linux executables, and dissecting process memory. In the computer security industry these skills are priceless, and scarce. The tutorials are filled with knowledge gained through first hand experience, and are complemented with frequent examples including source code.
Basic concepts of stationary processes; Sufficient statistics for binary Markov chains; The distribution of the number of axis-crossing; Upcrossings of a high level by a stationary process; Clipping a gaussian process; Estimation in ar(1) after hard limiting; Estimation in ar(p); Runs and estimates of correlations; Spectral analysis after clipping; Extremes in stationary time series; A central limit (ACL); Prediction in binary data.
The aim of this encyclopedia is to provide a comprehensive reference work on scientific and other scholarly research on the quality of life, including health-related quality of life research or also called patient-reported outcomes research. Since the 1960s two overlapping but fairly distinct research communities and traditions have developed concerning ideas about the quality of life, individually and collectively, one with a fairly narrow focus on health-related issues and one with a quite broad focus. In many ways, the central issues of these fields have roots extending to the observations and speculations of ancient philosophers, creating a continuous exploration by diverse explorers in diverse historic and cultural circumstances over several centuries of the qualities of human existence. What we have not had so far is a single, multidimensional reference work connecting the most salient and important contributions to the relevant fields. Entries are organized alphabetically and cover basic concepts, relatively well established facts, lawlike and causal relations, theories, methods, standardized tests, biographic entries on significant figures, organizational profiles, indicators and indexes of qualities of individuals and of communities of diverse sizes, including rural areas, towns, cities, counties, provinces, states, regions, countries and groups of countries.
Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The second edition is a substantial update of the first based on the authors’ experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter—group testing and splines. The computing has been completely updated, with the "emmeans" package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The solutions manual and teaching videos have also been updated. Features: Requires no prior experience with R, and offers an introduction to the essential features and functions of R Includes numerous examples from medicine, psychology, sports, ecology, and many other areas Integrates extensive R code and output Graphically demonstrates many of the features and properties of various analysis methods Offers a substantial number of exercises in all chapters, enabling use as a course text or for self-study Supplemented by a website with data sets, code, and teaching videos Analysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level. Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline. It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise.
