This book is aimed at Ph.D. and advanced M.S. students of statistics who need to understand modern statistical theory both in its exponential family structure and its applications, without requiring advanced mathematical preparation. Connections with logistic regression, survival analysis, Bayesian methods, and false discovery rates are emphasized.
Algorithms, Evidence, and Data Science
This engaging text simplifies complex topics in data science, statistics, and machine learning, making them accessible to readers. Enhanced with exercises, it encourages practical application and deeper understanding, ensuring a comprehensive grasp of these essential fields.
Algorithms, Evidence, and Data Science
Embark on an exhilarating exploration of the modern revolution in statistics led by two influential figures in the field. This book delves into groundbreaking methodologies and transformative ideas that have reshaped statistical practices. It highlights the impact of these innovations on various disciplines, making complex concepts accessible and engaging for readers. Through their insights, the authors illuminate the evolving landscape of data analysis and its significance in today's world.
Empirical Bayes Methods for Estimation, Testing, and Prediction
The book delves into the methodology of conducting numerous simultaneous estimates and tests, addressing the challenges posed by modern scientific advancements. Bradley Efron provides insights into statistical techniques that enable researchers to handle complex data analysis effectively, making it a valuable resource for those navigating the evolving landscape of scientific research.