Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all...

Five years ago, we authored “Text Mining: Predictive Methods for Analyzing Unstructured Information.” That book was geared mostly to professional practitioners, but was adaptable to course work with some effort by the instructor. Many topics were evolving, and this was one of the earliest efforts to collect material for predictive...

The publication of Society and Health: Sociology for Health Professionals represents the results of an information-gathering process that has extended over a 30-year career as a health professional. It reflects a determination to frame medical sociology as a multidisciplinary endeavor that must, of necessity, draw information from a wide...

Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new developments so that the methodologies can be properly implemented in the biomedical field. Responding to this need, Computational Methods in Biomedical Research explores important current and emerging...

Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for...

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied...

Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a balance between presentation of mathematical theory and development of numerical algorithms. Building on students’ skills in calculus...

This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is...

Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points.  By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical...

This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the...