Introduction to Modern Nonparametric Statistics

By James J. Higgins,

Book cover of Introduction to Modern Nonparametric Statistics

Book description

Guided by problems that frequently arise in actual practice, James Higgins' book presents a wide array of nonparametric methods of data analysis that researchers will find useful. It discusses a variety of nonparametric methods and, wherever possible, stresses the connection between methods. For instance, rank tests are introduced as special…

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Why read it?

1 author picked Introduction to Modern Nonparametric Statistics as one of their favorite books. Why do they recommend it?

This is one of my favorite underappreciated statistics books of all time. Non-parametric statistics can be otherwise described as statistics without assumptions. The entire goal of this field of study is to prove X is greater than Y without making any assumptions about the underlying distributions of X or Y. The methods are different, and they require more data than other methods, but the learning journey is invaluable.

I personally believe that modern machine learning is simply the modeling section of the school of non-parametric statistics. Working through this book will give you a much deeper understanding of why…

From Chris' list on mathematics for quant finance.

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