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This volume discusses an important area of statistics and
highlights the most important statistical advances. It is divided
into four sections: statistics in the life and medical sciences,
business and social science, the physical sciences and engineering,
and theory and methods of statistics.
This book provides a coherent framework for understanding shrinkage
estimation in statistics. The term refers to modifying a classical
estimator by moving it closer to a target which could be known a
priori or arise from a model. The goal is to construct estimators
with improved statistical properties. The book focuses primarily on
point and loss estimation of the mean vector of multivariate normal
and spherically symmetric distributions. Chapter 1 reviews the
statistical and decision theoretic terminology and results that
will be used throughout the book. Chapter 2 is concerned with
estimating the mean vector of a multivariate normal distribution
under quadratic loss from a frequentist perspective. In Chapter 3
the authors take a Bayesian view of shrinkage estimation in the
normal setting. Chapter 4 introduces the general classes of
spherically and elliptically symmetric distributions. Point and
loss estimation for these broad classes are studied in subsequent
chapters. In particular, Chapter 5 extends many of the results from
Chapters 2 and 3 to spherically and elliptically symmetric
distributions. Chapter 6 considers the general linear model with
spherically symmetric error distributions when a residual vector is
available. Chapter 7 then considers the problem of estimating a
location vector which is constrained to lie in a convex set. Much
of the chapter is devoted to one of two types of constraint sets,
balls and polyhedral cones. In Chapter 8 the authors focus on loss
estimation and data-dependent evidence reports. Appendices cover a
number of technical topics including weakly differentiable
functions; examples where Stein's identity doesn't hold; Stein's
lemma and Stokes' theorem for smooth boundaries; harmonic,
superharmonic and subharmonic functions; and modified Bessel
functions.
The present volume consists of papers written by students,
colleagues and collaborators of Sreenivasa Rao Jammalamadaka from
various countries, and covers a variety of research topics which he
enjoys and contributed immensely to.
The present volume consists of papers written by students,
colleagues and collaborators of Sreenivasa Rao Jammalamadaka from
various countries, and covers a variety of research topics which he
enjoys and contributed immensely to.
Now in its second edition, this handbook collects authoritative
contributions on modern methods and tools in statistical
bioinformatics with a focus on the interface between computational
statistics and cutting-edge developments in computational biology.
The three parts of the book cover statistical methods for
single-cell analysis, network analysis, and systems biology, with
contributions by leading experts addressing key topics in
probabilistic and statistical modeling and the analysis of massive
data sets generated by modern biotechnology. This handbook will
serve as a useful reference source for students, researchers and
practitioners in statistics, computer science and biological and
biomedical research, who are interested in the latest developments
in computational statistics as applied to computational biology.
Exactly what is the state of the art in statistics as we move forward into the 21st century? What promises, what trends does its future hold? Through the reflections of 70 of the world's leading statistical methodologists, researchers, theorists, and practitioners, Statistics in the 21st Century answers those questions.
Originally published in the Journal of the American Statistical Association, this collection of vignettes examines our statistical past, comments on our present, and speculates on our future. Although the coverage is broad and the topics diverse, it reveals the essential intellectual unity of the field as we see the same themes recurring in different contexts. We see how the development of statistics has been driven by the unprecedented and still growing range of applications, by the explosion in computer technology, and by the new types of data that continue to emerge and advance the discipline.
Organized around major areas of application and leading up to vignettes on theory and methods, Statistics in the 21st Century forms a landmark record of the progress and perceived future of the discipline. No student, researcher, or practitioner of statistics should miss this extraordinary opportunity to view the past, present, and future world of statistics through the eyes of its foremost thinkers.
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