In this video, I'm sharing my top pick for "the" book for mathematical statistics. This book is an essential resource for students and practicing statisticians, data scientists, and ML engineers in the field of mathematical statistics.
If you're looking to learn about statistical theory and the methods used in mathematical statistics, then you need to get yourself a copy of Casella and Burger's Statistical Inference! It's a dense read, but it's well worth the investment! In this book, you'll find everything you need to understand and apply the principles of statistical estimation, inference, confidence intervals, probability, and probability distributions.
Script:
Statistical Inference by Casella and Berger. It is hard to understate the value of this book. Even if all someone reads is the first two chapters, that would be sufficient for an entry-level position in data science. The problem set is fun and challenging, and you will probably never get through all of it. In my masters program, I am not sure if we even solved half of the problems, and that was with two semesters dedicated to this book! There are a number of interesting tables, like the one at the back of the book, which shows how many of the popular probability distributions are related. This is a graduate-level textbook. This is a must-have for anyone trying to learn mathematical statistics. The book is heavy on definitions and expects the reader to play with those definitions in the form of applications, derivations, and occasional proofs. Though I have been out of school for many years, I will still pick this book up, pick a section at random, and read through it. It keeps me sharp, and even if I read a section before, I often learn something new on a third or fourth pass. There is a reason this continues to be a recommended book for those looking for rigorous mathematical statistics.
If you're looking to learn about statistical theory and the methods used in mathematical statistics, then you need to get yourself a copy of Casella and Burger's Statistical Inference! It's a dense read, but it's well worth the investment! In this book, you'll find everything you need to understand and apply the principles of statistical estimation, inference, confidence intervals, probability, and probability distributions.
Script:
Statistical Inference by Casella and Berger. It is hard to understate the value of this book. Even if all someone reads is the first two chapters, that would be sufficient for an entry-level position in data science. The problem set is fun and challenging, and you will probably never get through all of it. In my masters program, I am not sure if we even solved half of the problems, and that was with two semesters dedicated to this book! There are a number of interesting tables, like the one at the back of the book, which shows how many of the popular probability distributions are related. This is a graduate-level textbook. This is a must-have for anyone trying to learn mathematical statistics. The book is heavy on definitions and expects the reader to play with those definitions in the form of applications, derivations, and occasional proofs. Though I have been out of school for many years, I will still pick this book up, pick a section at random, and read through it. It keeps me sharp, and even if I read a section before, I often learn something new on a third or fourth pass. There is a reason this continues to be a recommended book for those looking for rigorous mathematical statistics.
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