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Measurement error arises ubiquitously in applications and has been
of long-standing concern in a variety of fields, including medical
research, epidemiological studies, economics, environmental
studies, and survey research. While several research monographs are
available to summarize methods and strategies of handling different
measurement error problems, research in this area continues to
attract extensive attention. The Handbook of Measurement Error
Models provides overviews of various topics on measurement error
problems. It collects carefully edited chapters concerning issues
of measurement error and evolving statistical methods, with a good
balance of methodology and applications. It is prepared for readers
who wish to start research and gain insights into challenges,
methods, and applications related to error-prone data. It also
serves as a reference text on statistical methods and applications
pertinent to measurement error models, for researchers and data
analysts alike. Features: Provides an account of past development
and modern advancement concerning measurement error problems
Highlights the challenges induced by error-contaminated data
Introduces off-the-shelf methods for mitigating deleterious impacts
of measurement error Describes state-of-the-art strategies for
conducting in-depth research
Bayesian Inference for Partially Identified Models: Exploring the
Limits of Limited Data shows how the Bayesian approach to inference
is applicable to partially identified models (PIMs) and examines
the performance of Bayesian procedures in partially identified
contexts. Drawing on his many years of research in this area, the
author presents a thorough overview of the statistical theory,
properties, and applications of PIMs. The book first describes how
reparameterization can assist in computing posterior quantities and
providing insight into the properties of Bayesian estimators. It
next compares partial identification and model misspecification,
discussing which is the lesser of the two evils. The author then
works through PIM examples in depth, examining the ramifications of
partial identification in terms of how inferences change and the
extent to which they sharpen as more data accumulate. He also
explains how to characterize the value of information obtained from
data in a partially identified context and explores some recent
applications of PIMs. In the final chapter, the author shares his
thoughts on the past and present state of research on partial
identification. This book helps readers understand how to use
Bayesian methods for analyzing PIMs. Readers will recognize under
what circumstances a posterior distribution on a target parameter
will be usefully narrow versus uselessly wide.
Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments examines the consequences and Bayesian remedies in those cases where the explanatory variable cannot be measured with precision.
The author explores both measurement error in continuous variables and misclassification in discrete variables, and shows how Bayesian methods might be used to allow for mismeasurement. A broad range of topics, from basic research to more complex concepts such as "wrong-model" fitting, make this a useful research work for practitioners, students and researchers in biostatistics and epidemiology."
Bayesian Inference for Partially Identified Models: Exploring the
Limits of Limited Data shows how the Bayesian approach to inference
is applicable to partially identified models (PIMs) and examines
the performance of Bayesian procedures in partially identified
contexts. Drawing on his many years of research in this area, the
author presents a thorough overview of the statistical theory,
properties, and applications of PIMs. The book first describes how
reparameterization can assist in computing posterior quantities and
providing insight into the properties of Bayesian estimators. It
next compares partial identification and model misspecification,
discussing which is the lesser of the two evils. The author then
works through PIM examples in depth, examining the ramifications of
partial identification in terms of how inferences change and the
extent to which they sharpen as more data accumulate. He also
explains how to characterize the value of information obtained from
data in a partially identified context and explores some recent
applications of PIMs. In the final chapter, the author shares his
thoughts on the past and present state of research on partial
identification. This book helps readers understand how to use
Bayesian methods for analyzing PIMs. Readers will recognize under
what circumstances a posterior distribution on a target parameter
will be usefully narrow versus uselessly wide.
Data has become a factor of production, like labor and steel, and
is driving a new data-centered economy. The Data rEvolution is
about data volume, variety, velocity and value. It is about new
ways to organize and manage data for rapid processing using tools
like Hadoop and MapReduce. It is about the explosion of new tools
for "connecting the dots" and increasing knowledge, including link
analysis, temporal analysis and predictive analytics. It is about a
vision of "analytics for everyone" that puts sophisticated
statistics into the hands of all. And, it is about using visual
analytics to parse the data and literally see new relationships and
insights on the fly. As the data and tools become democratized, we
will see a new world of experimentation and creative
problem-solving, where data comes from both inside and outside the
organization. Your own data is not enough. This report is a
must-read for IT and business leaders who want to maximize the
value of data for their organization.
This book addresses statistical challenges posed by inaccurately
measuring explanatory variables, a common problem in biostatistics
and epidemiology. The author explores both measurement error in
continuous variables and misclassification in categorical
variables. He also describes the circumstances in which it is
necessary to explicitly adjust for imprecise covariates using the
Bayesian approach and a Markov chain Monte Carlo algorithm. The
book offers a mix of basic and more specialized topics and provides
mathematical details in the final sections of each chapter. Because
of its dual approach, the book is a useful reference for
biostatisticians, epidemiologists, and students.
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