Exact statistical inference may be employed in diverse fields of
science and technology. As problems become more complex and sample
sizes become larger, mathematical and computational difficulties
can arise that require the use of approximate statistical methods.
Such methods are justified by asymptotic arguments but are still
based on the concepts and principles that underlie exact
statistical inference. With this in perspective, this book presents
a broad view of exact statistical inference and the development of
asymptotic statistical inference, providing a justification for the
use of asymptotic methods for large samples. Methodological results
are developed on a concrete and yet rigorous mathematical level and
are applied to a variety of problems that include categorical data,
regression, and survival analyses. This book is designed as a
textbook for advanced undergraduate or beginning graduate students
in statistics, biostatistics, or applied statistics but may also be
used as a reference for academic researchers.
General
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