
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard
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This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested
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The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place
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A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic,
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Many arborists learn tree work practices without fully understanding the biological and physiological principles behind them. However, outcomes for the health and longevity of trees are greatly improved when an arborist understands the science behind the care of tree root systems and crowns. In
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This book presents the fundamentals of molecular biophysics, and highlights the connection between molecules and biological phenomena, making it an important text across a variety of science disciplines. The topics covered in the book include: Phase transitions that occur in biosystems (protein
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Praise for the First Edition «The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful.» —The American Statistician Fully revised to reflect the latest methodologies and emerging applications,
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An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculus Regression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response
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Master the fundamentals of regression without learning calculus with this one-stop resource The newly and thoroughly revised 3rd Edition of Applied Regression Modeling delivers a concise but comprehensive treatment of the application of statistical regression analysis for those with little or
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Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational,
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