Analyzing the Large Number of Variables in Biomedical and Satellite Imagery - by Phillip I Good Paperback

Book Synopsis

This book grew out of an online interactive offered through statcourse.com, and it soon became apparent to the author that the course was too limited in terms of time and length in light of the broad backgrounds of the enrolled students. The statisticians who took the course needed to be brought up to speed both on the biological context as well as on the specialized statistical methods needed to handle large arrays. Biologists and physicians, even though fully knowledgeable concerning the procedures used to generate microaarrays, EEGs, or MRIs, needed a full introduction to the resampling methods--the bootstrap, decision trees, and permutation tests, before the specialized methods applicable to large arrays could be introduced. As the intended audience for this book consists both of statisticians and of medical and biological research workers as well as all those research workers who make use of satellite imagery including agronomists and meteorologists, the book provides a step-by-step approach to not only the specialized methods needed to analyze the data from microarrays and images, but also to the resampling methods, step-down multi-comparison procedures, multivariate analysis, as well as data collection and pre-processing. While many alternate techniques for analysis have been introduced in the past decade, the author has selected only those techniques for which software is available along with a list of the available links from which the software may be purchased or downloaded without charge. Topical coverage includes: very large arrays; permutation tests; applying permutation tests; gathering and preparing data for analysis; multiple tests; bootstrap; applying the bootstrap; classification methods; decision trees; and applying decision trees.

From the Back Cover

A comprehensive presentation of statistical and data mining methods for analyzing high-dimensional data

Drawing from the author's extensive experience in the field, Analyzing the Large Number of Variables in Biomedical and Satellite Imagery provides an authoritative, step-by-step presentation of the specialized methods needed to analyze the large data sets that arise from the study of microarrays, EEGs, MEGs, MRIs, and other biomedical and satellite images. The book discusses both the biological context in which data is collected as well as the specialized statistical methods needed to handle large arrays.

The author begins with an introductory chapter, which addresses problems that arise when analyzing medical data and presents potential solutions. Focusing on the research needs of both statisticians and medical researchers, subsequent chapters provide a step-by-step approach to solving these common research problems, not only through specialized methods for analyzing data from microarrays and images, but also through resampling methods, step-down multi-comparison procedures, multivariate analysis, data collection, and pre-processing techniques. The following methods are first described and then illustrated with examples from biomedical literature:

• Permutation tests

• Multiple tests of hypotheses

• Bootstrap

• Classification methods

• Decision trees

While many alternate techniques for analyzing high-dimensional data have been introduced in the past decade, the author has a unique approach that features only those techniques for which software is available. Throughout the book, links are provided to the many specialized programs that may be downloaded as well as a number of program listings, and an R primer is also included in an appendix. A glossary of statistical terminology is included and provides a refresher for key terms and ideas.

Analyzing the Large Number of Variables in Biomedical and Satellite Imagery serves as an excellent supplement for courses on data analysis at the upper-undergraduate and graduate levels. The book is also a valuable resource for statisticians, physicians, and biological research workers who deal with medical images in their daily work.

About the Author

Phillip I. Good, PhD, is Operations Manager at Information Research, a consulting firm specializing in statistical solutions for private and public organizations. He has published more than thirty scholarly works and more than six hundred popular articles. Dr. Good is the author of Introduction to Statistics Through Resampling Methods and R/S-PLUS(R) and Introduction to Statistics Through Resampling Methods and Microsoft Office Excel(R), and coauthor of Common Errors in Statistics (and How to Avoid Them), Third Edition, all published by Wiley.