Analyzing the Large Number of Variables in Biomedical and Satellite Imagery

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.