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Sufficient Dimension Reduction: Methods and Applications with R

Bing Li
May 01, 2018

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of...

Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS

Hassan M. Khormi, Lalit Kumar
April 30, 2018

Master GIS Applications on Modelling and Mapping the Risks of Diseases Infections transmitted by mosquitoes, ticks, triatomine bugs, sandflies, and black flies cause significant rates of death and disease, especially in developing countries. Why are certain places more susceptible to vector-borne...

Generalized Linear Models and Extensions: Fourth Edition

James W. Hardin, Joseph M. Hilbe
April 27, 2018

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions—a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these...

A Practical Guide to Age-Period-Cohort Analysis: The Identification Problem and Beyond

Wenjiang Fu
April 25, 2018

Age-Period-Cohort analysis has a wide range of applications, from chronic disease incidence and mortality data in public health and epidemiology, to many social events (birth, death, marriage, etc) in social sciences and demography, and most recently investment, healthcare and pension contribution...

Mathematical Theory of Bayesian Statistics

Sumio Watanabe
April 23, 2018

Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method. Recent research has uncovered several mathematical laws in Bayesian statistics, by which...

Probabilistic Foundations of Statistical Network Analysis

Harry Crane
April 19, 2018

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic...

Spectral Feature Selection for Data Mining

Zheng Alan Zhao, Huan Liu
April 18, 2018

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified...

An Introduction to Generalized Linear Models

Annette J. Dobson, Adrian G. Barnett
April 13, 2018

An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection,...

A Gentle Introduction to Stata

Alan C. Acock
April 12, 2018

Alan C. Acock's A Gentle Introduction to Stata, Sixth Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able not only to use Stata well but also to learn new aspects of Stata. Acock assumes that the user is not...

Human-in-the-Loop: Probabilistic Modeling of an Aerospace Mission Outcome

Ephraim Suhir
April 05, 2018

Improvements in safety in the air and in space can be achieved through better ergonomics, better work environments, and other efforts of the traditional avionic psychology that directly affect human behaviors and performance. Not limited to just the aerospace field, this book discusses adaptive...

Feature Engineering for Machine Learning and Data Analytics

Guozhu Dong, Huan Liu
April 04, 2018

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the...

Analysis of Correlated Data with SAS and R

Mohamed M. Shoukri
April 02, 2018

Analysis of Correlated Data with SAS and R: 4th edition presents an applied treatment of recently developed statistical models and methods for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in SAS and packages in R for exploring data,...