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Contributions on Theory of Mathematical Statistics (Hardcover, 1st ed. 2020)
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Contributions on Theory of Mathematical Statistics (Hardcover, 1st ed. 2020)
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This volume is a reorganized edition of Kei Takeuchi's works on
various problems in mathematical statistics based on papers and
monographs written since the 1960s on several topics in
mathematical statistics and published in various journals in
English and in Japanese. They are organized into seven parts, each
of which is concerned with specific topics and edited to make a
consistent thesis. Sometimes expository chapters have been added.
The topics included are as follows: theory of statistical
prediction from a non-Bayesian viewpoint and analogous to the
classical theory of statistical inference; theory of robust
estimation, concepts, and procedures, and its implications for
practical applications; theory of location and scale
covariant/invariant estimations with derivation of explicit forms
in various cases; theory of selection and testing of parametric
models and a comprehensive approach including the derivation of the
Akaike's Information Criterion (AIC); theory of randomized designs,
comparisons of random and conditional approaches, and of randomized
and non-randomized designs, with random sampling from finite
populations considered as a special case of randomized designs and
with some separate independent papers included. Theory of
asymptotically optimal and higher-order optimal estimators are not
included, since most of them already have been published in the
Joint Collected Papers of M. Akahira and K. Takeuchi. There are
some topics that are not necessarily new, do not seem to have
attracted many theoretical statisticians, and do not appear to have
been systematically dealt with in textbooks or expository
monographs. One purpose of this volume is to give a comprehensive
view of such problems as well.
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