Mathematics and Statistics: Statistics and epidemiology

Provides an accessible introduction to multiple imputation for handling missing data. Examines various missing-data problems and presents strategies for tackling them. Includes many examples using real data.

This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures.

Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems. Features new chapters on computer-intensive methods and new sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines. Many more exercises and examples using both Stata and SAS.

Now available as an ebook, this text provides a thorough introduction to basic concepts and methods in applied spatial statistics as well as a detailed treatment of some of the more recent methods in spatial statistics useful for public health studies.

Covers the main data mining techniques through carefully selected case studies. Describes code and approaches that can be easily reproduced or adapted to your own problems. Requires no prior experience with R.

This new edition-more than 50% new and revised- is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems.

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process.

Data Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more.

This accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models.

Provides a clear introduction and a comprehensive account of multilevel models. New methodological developments and applications are explored. Written by a leading expert in the field of multilevel methodology. Illustrated throughout with real-life examples, explaining theoretical concepts.

This third edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.

This book introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets.

How to work with the different programming aspects of R: develop practical solutions out of real world problems in a scalable fashion.

Now available as an ebook. Perform data analysis with R quickly and efficiently with the task-oriented recipes in this cookbook. This book will help both beginners and experienced data programmers.

This hands-on book teaches novices and experienced R users how to write efficient R code. Provides practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++.

Teaches you how to perform development-time testing using the testthat package, allowing you to ensure that your code works as intended. The book also teaches run-time testing using the assertive package; enabling your users to correctly run your code.

Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). Colour graphics are used throughout.

The dynamic, student focused textbook provides step-by-step instruction in the use of R and of statistical language as a general research tool. It is ideal for anyone hoping to complete an introductory course in statistics or prepare for more advanced statistical courses.