Statistical Prediction and Machine Learning

Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.

One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors? teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.

Key Features:

• Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science.
• Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy.
• Integrates statistical theory with machine learning algorithms.
• Includes potential methodological developments in data science.

Preface 1. Two Cultures in Data Science 2. Fundamental Instruments 3. Sensitivity and Specificity Trade-off 4. Bias and Variation Trade-off 5. Linear Prediction 6. Nonlinear Prediction 7. Minimum Risk Classification 8. Support Vectors and Duality Theorem 9. Decision Trees and Range Regressions 10. Unsupervised Learning and Optimization 11. Simultaneous Learning and Multiplicity Bibliography Index

John T. Chen is a professor of Statistics at Bowling Green State University. He completed his postdoctoral training at McMaster University (Canada) after earning a PhD degree in statistics at the University of Sydney (Australia). John has published research papers in statistics journals such as Biometrika as well as in medicine journals such as the Annals of Neurology.

Clement Lee is a data scientist in a private firm in New York. He earned a Master’s degree in applied mathematics from New York University, after graduating from Princeton University in computer science. Clement enjoys spending time with his beloved wife Belinda and their son Pascal.

Lincy Y. Chen is a data scientist at JP Morgan Chase & Co. She graduated from Cornell University, winning the Edward M. Snyder Prize in Statistics. Lincy has published papers regarding refinements of machine learning methods.