A Sound Basis for the Theory of Statistical Inference Measuring
Statistical Evidence Using Relative Belief provides an overview of
recent work on developing a theory of statistical inference based
on measuring statistical evidence. It shows that being explicit
about how to measure statistical evidence allows you to answer the
basic question of when a statistical analysis is correct. The book
attempts to establish a gold standard for how a statistical
analysis should proceed. It first introduces basic features of the
overall approach, such as the roles of subjectivity, objectivity,
infinity, and utility in statistical analyses. It next discusses
the meaning of probability and the various positions taken on
probability. The author then focuses on the definition of
statistical evidence and how it should be measured. He presents a
method for measuring statistical evidence and develops a theory of
inference based on this method. He also discusses how statisticians
should choose the ingredients for a statistical problem and how
these choices are to be checked for their relevance in an
application.
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