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Spatio–Temporal Methods in Environmental Epidemiology with R
Spatio-Temporal Methods in Environmental Epidemiology with R, like its First Edition, explores the interface between environmental epidemiology and spatio-temporal modeling. It links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems, it shows how recent advances in methodology can assess the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. New additions to the Second Edition include: a thorough exploration of the underlying concepts behind knowledge discovery through data; a new chapter on extracting information from dat...
Oxford Handbook of Epidemiology for Clinicians
This text provides all the information required by students and junior doctors who need to understand and translate key epidemiological concepts into medical practice.
Generalized Linear Mixed Models
Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture – linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory. Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, t...
A Course in the Large Sample Theory of Statistical Inference
Provides accessible introduction to large sample theory with moving alternatives Elucidates mathematical concepts using simple practical examples Includes problem sets and solutions for each chapter Uses the moving alternative formulation developed by LeCam but requires a minimum of mathematical prerequisites
R for Business Analytics
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. T...
WHO Urban Health Initiative in Accra, Ghana
Ambient and household air pollution are a major cause of death and disease globally. This public health threat is being increased due to the rapid urbanization process and environmental degradation that characterizes the 21st century and that have a higher impact in developing countries. The World Health Organization (WHO) Urban Health Initiative (UHI) is implemented as a response to the World Health Assembly (WHA) Resolution 68.8 from May 2015, which requests WHO to build health sector capacity to work with other sectors, support countries to identify effective policy measures, track progress, and continue to update the evidence for health impacts of air pollution. WHO conducted a pilot pro...
Environmental Sustainability
This book examines the global challenges of air pollution and its consequences at domestic and international levels. Industrialization and logistical operations are the critical factors of carbon emissions, damaging fauna and flora. In addition, air pollution adversely affects human health. As such, this book discusses possible solutions to mitigate air pollution both domestically and internationally.
A First Course in Causal Inference
The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics. Key Features: All R code and data sets available at Harvard Dataverse. Solutions manual available for instructors. Includes over 100 exercises. This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments.
Nonparametric Statistical Methods Using R
Praise for the first edition: “This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.” -The American Statistician This thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and tre...
Coronary Heart Disease Epidemiology
Heart attack (ischaemic heart disease or coronary heart disease) as one of a group of cardiovascular diseases, is one of the main causes of death (over 30 million/year) in the developed and developing world. The dual aim of this book is to review the well-established risk factors in CHD and to look forward to disease prevention, equipped with lessons from the past. The book covers etiology to public health, including studies within a single population and international studies, important areas of methodological development, trials to test preventive strategies, and the application of epidemiological and other knowledge to the development of public health policy for the prevention of widespread disease. It is an all-encompassing work containing contributions from the world authorities in the field.
