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Features: Second edition has been updated with a new chapter on
Nonparametric Estimation; a significant update to the chapter on
Statistical Decision Theory; and other updates throughout No
requirement for heavy calculus, and simple questions throughout the
text help students check their understanding of the material Each
chapter also includes a set of exercises that range in level of
difficulty Self-contained, and can be used by the students to
understand the theory Chapters and sections marked by asterisks
contain more advanced topics and may be omitted Special chapters on
linear models and nonparametric statistics show how the main
theoretical concepts can be applied to well-known and frequently
used statistical tools
This volume presents selections of Peter J. Bickel's major papers,
along with comments on their novelty and impact on the subsequent
development of statistics as a discipline. Each of the eight parts
concerns a particular area of research and provides new commentary
by experts in the area. The parts range from Rank-Based
Nonparametrics to Function Estimation and Bootstrap Resampling.
Peter's amazing career encompasses the majority of statistical
developments in the last half-century or about about half of the
entire history of the systematic development of statistics. This
volume shares insights on these exciting statistical developments
with future generations of statisticians. The compilation of
supporting material about Peter's life and work help readers
understand the environment under which his research was conducted.
The material will also inspire readers in their own research-based
pursuits. This volume includes new photos of Peter Bickel, his
biography, publication list, and a list of his students. These give
the reader a more complete picture of Peter Bickel as a teacher, a
friend, a colleague, and a family man.
This volume presents selections of Peter J. Bickel's major papers,
along with comments on their novelty and impact on the subsequent
development of statistics as a discipline. Each of the eight parts
concerns a particular area of research and provides new commentary
by experts in the area. The parts range from Rank-Based
Nonparametrics to Function Estimation and Bootstrap Resampling.
Peter's amazing career encompasses the majority of statistical
developments in the last half-century or about about half of the
entire history of the systematic development of statistics. This
volume shares insights on these exciting statistical developments
with future generations of statisticians. The compilation of
supporting material about Peter's life and work help readers
understand the environment under which his research was conducted.
The material will also inspire readers in their own research-based
pursuits. This volume includes new photos of Peter Bickel, his
biography, publication list, and a list of his students. These give
the reader a more complete picture of Peter Bickel as a teacher, a
friend, a colleague, and a family man.
This volume is a collection of papers presented at a conference
held in Shoresh Holiday Resort near Jerusalem, Israel, in December
2000 organized by the Israeli Ministry of Science, Culture and
Sport. The theme of the conference was "Foundation of Statistical
Inference: Applications in the Medical and Social Sciences and in
Industry and the Interface of Computer Sciences." The following is
a quotation from the Program and Abstract booklet of the
conference. "Over the past several decades, the field of statistics
has seen tremendous growth and development in theory and
methodology. At the same time, the advent of computers has
facilitated the use of modern statistics in all branches of
science, making statistics even more interdisciplinary than in the
past; statistics, thus, has become strongly rooted in all empirical
research in the medical, social, and engineering sciences. The
abundance of computer programs and the variety of methods available
to users brought to light the critical issues of choosing models
and, given a data set, the methods most suitable for its analysis.
Mathematical statisticians have devoted a great deal of effort to
studying the appropriateness of models for various types of data,
and defining the conditions under which a particular method work. "
In 1985 an international conference with a similar title* was held
in Is rael. It provided a platform for a formal debate between the
two main schools of thought in Statistics, the Bayesian, and the
Frequentists."
This book is about estimation in situations where we believe we have enough knowledge to model some features of the data parametrically, but are unwilling to assume anything for other features. Such models have arisen in a wide variety of contexts in recent years, particularly in economics, epidemiology, and astronomy. The complicated structure of these models typically requires us to consider nonlinear estimation procedures which often can only be implemented algorithmically. The theory of these procedures is necessarily based on asymptotic approximations.
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