Multivariate Density Estimation : Theory, Practice, and Visualization (Wiley Series in Probability and Statistics)

Rating:
Summary: You are my denstiny.
Review: I picked up this book because I'm interested in theories of free will vs. destiny.
After I got about 3/4 a the way through this book I came to the conclusion I was not gonna find any answers to my quest in this book.
On a side note I did pick up some advise on how to estimate the thickness of an object. For example this here book is about 2 inches thick. The pencil on my desk is about a quarter inch thick. Of course I did not need to spend a hundred bucks to figure that out and of what practical use it is escapes me.
My quest continues but after reading this book I can tell that I am much denser than I originally suspected.

Rating:
Summary: Excellent treatment of the histogram
Review: This self-contained title represents a thorough treatment of both univariate and multivariate density estimation, with an emphasis on multivariate applications not found in other texts. A valuable, and perhaps unique, feature of this book is its clear discussion of the theory of kernel density estimation in terms of the conventional histogram, the latter being quite intuitive to most students.

The author uses applied mathematics, expected value operations, and asymptotic theory to draw general conclusions about the relative performance between different density estimation techniques. The purpose is to provide estimators that are "optimal" for higher dimensioned problems, since the computational burden grows exponentially with higher dimensions (thus making exploratory data analysis more expensive). With this in mind, the author is justified to show how the thorough treatment of theory relates to practice and visualization.

Ideas are reinforced by many problems at the end of each chapter (I regret that the answers to these problems are not included, or would have otherwise preferred just a few more examples). One nice feature is the author puts boxes around the most important theorems and results, making them easier to identify for reference purposes later. There is an emphasis on the way univariate concepts extend to lower dimensional multivariate problems, so that only a minority of the text might be considered exclusively multivariate in scope. The book contains a valuable list of references and a terse but useful appendix on 3-D computer visualization.

As a result, this text educates on more than density estimation, but does so assuming a fairly sophisticated level of audience education and background beyond the first few chapters. Since most of the terminology and basic concepts are defined early on, yet the most interesting applications and methods are discussed toward the end, this title is not easily skimmed. I have found this book cited often by other texts in this fields.