Human endeavor in the hundreds of fields that comprise human knowledge
(for example, modern universities may offer graduate degrees in over 100
programs, some of them having sub-areas—see Syracuse University A999,
pp. 7-9) for an example with 116 graduate programs) involves the construc-
construction of statistical models to describe fundamental variables. Of the variety
of possible models, probability distributions are the most widely used ones.
This book deals with how to model (or, how to fit) continuous probability
distribution models to data.
The area of fitting distributions to data has seen explosive growth in re-
recent years. In view of this and the widespread need for fitting methods in
virtually all areas of endeavor, we believe there is a need for a text that can
be used in applied programs (including graduate programs in the mathemat-
mathematical sciences, where many research topics in this area will be explored), and
as a reference work by researchers and practitioners. This book therefore
includes the following: a detailed exposition of the Generalized Lambda Dis-
Distribution (GLD), relevant results related to the Generalized Bootstrap (GB)
and Monte Carlo Simulation (MC), and the necessary tables, algorithms,
and computer programs for fitting continuous probability distributions to
data.