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Nonparametric Statistical Methods For Complete and Censored Data (Paperback)
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Nonparametric Statistical Methods For Complete and Censored Data (Paperback)
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Balancing the "cookbook" approach of some texts with the more
mathematical approach of others, Nonparametric Statistical Methods
for Complete and Censored Data introduces commonly used
non-parametric methods for complete data and extends those methods
to right censored data analysis. Whenever possible, the authors
derive their methodology from the general theory of statistical
inference and introduce the concepts intuitively for students with
minimal backgrounds. Derivations and mathematical details are
relegated to appendices at the end of each chapter, which allows
students to easily proceed through each chapter without becoming
bogged down in a lot of mathematics. In addition to the
nonparametric methods for analyzing complete and censored data, the
book covers optimal linear rank statistics, clinical equivalence,
analysis of block designs, and precedence tests. To make the
material more accessible and practical, the authors use SAS
programs to illustrate the various methods included. Exercises in
each chapter, SAS code, and a clear, accessible presentation make
this an outstanding text for a one-semester senior or
graduate-level course in nonparametric statistics for students in a
variety of disciplines, from statistics and biostatistics to
business, psychology, and the social scientists. Prerequisites:
Students will need a solid background in calculus and a
two-semester course in mathematical statistics.
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