For over 200 years, practitioners have been developing parametric
families of probability distributions for data analysis. More recently,
an active development of nonparametric and semiparametric families has
occurred. This book includes an extensive discussion of a wide variety
of distribution families--nonparametric, semiparametric and
parametric--some well known and some not. An all-encompassing view is
taken for the purpose of identifying relationships, origins and
structures of the various families. A unified methodological approach
for the introduction of parameters into families is developed, and the
properties that the parameters imbue a distribution are clarified. These
results provide essential tools for intelligent choice of models for
data analysis. Many of the results given are new and have not previously
appeared in print. This book provides a comprehensive reference for
anyone working with nonnegative data.