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In fields such as biology, medical sciences, sociology, and
economics researchers often face the situation where the number of
available observations, or the amount of available information, is
sufficiently small that approximations based on the normal
distribution may be unreliable. Theoretical work over the last
quarter-century has led to new likelihood-based methods that lead
to very accurate approximations in finite samples, but this work
has had limited impact on statistical practice. This book
illustrates by means of realistic examples and case studies how to
use the new theory, and investigates how and when it makes a
difference to the resulting inference. The treatment is oriented
towards practice and comes with code in the R language (available
from the web) which enables the methods to be applied in a range of
situations of interest to practitioners. The analysis includes some
comparisons of higher order likelihood inference with bootstrap or
Bayesian methods. Author resource page: http:
//www.isib.cnr.it/~brazzale/AA/
Sir David Cox is among the most important statisticians of the past
half-century. He has made pioneering and highly influential
contributions to a uniquely wide range of topics in statistics and
applied probability. His teaching has inspired generations of
students, and many well-known researchers have begun as his
graduate students or have worked with him at early stages of their
careers. Legions of others have been stimulated and enlightened by
the clear, concise, and direct exposition exemplified by his many
books, papers, and lectures. This book presents a collection of
chapters by major statistical researchers who attended a conference
held at the University of Neuchatel in July 2004 to celebrate David
Cox's 80th birthday. Each chapter is carefully crafted and
collectively present current developments across a wide range of
research areas from epidemiology, environmental science, finance,
computing and medicine. Edited by Anthony Davison, Ecole
Polytechnique Federale de Lausanne, Switzerland; Yadolah Dodge,
University of Neuchatel, Switzerland; and N. Wermuth, Goteborg
University, Sweden, with chapters by Ole E. Barndorff-Nielsen,
Sarah C. Darby, Christina Davies, Peter J. Diggle, David Firth,
Peter Hall, Valerie S. Isham, Kung-Yee Liang, Peter McCullagh, Paul
McGale, Amilcare Porporato, Nancy Reid, Brian D. Ripley, Ignacio
Rodriguez-Iturbe, Andrea Rotnitzky, Neil Shephard, Scott L. Zeger,
and including a brief biography of David Cox, this book is suitable
for students of statistics, epidemiology, environmental science,
finance, computing and medicine, and academic and practising
statisticians.
Models and likelihood are the backbone of modern statistics and data analysis. The coverage is unrivalled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics. Anthony Davison blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practicioners. Its comprehensive coverage makes this the standard text and reference in the subject.
This book gives a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis. Applications include stratified data; finite populations; censored and missing data; linear, nonlinear, and smooth regression models; classification; time series and spatial problems. Special features of the book include: extensive discussion of significance tests and confidence intervals; material on various diagnostic methods; and methods for efficient computation, including improved Monte Carlo simulation. Each chapter includes both practical and theoretical exercises. Included with the book is a disk of purpose-written S-Plus programs for implementing the methods described in the text. Computer algorithms are clearly described, and computer code is included on a 3-inch, 1.4M disk for use with IBM computers and compatible machines. Users must have the S-Plus computer application.
Models and likelihood are the backbone of modern statistics. This
2003 book gives an integrated development of these topics that
blends theory and practice, intended for advanced undergraduate and
graduate students, researchers and practitioners. Its breadth is
unrivaled, with sections on survival analysis, missing data, Markov
chains, Markov random fields, point processes, graphical models,
simulation and Markov chain Monte Carlo, estimating functions,
asymptotic approximations, local likelihood and spline regressions
as well as on more standard topics such as likelihood and linear
and generalized linear models. Each chapter contains a wide range
of problems and exercises. Practicals in the S language designed to
build computing and data analysis skills, and a library of data
sets to accompany the book, are available over the Web.
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