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Biological and biomedical studies have entered a new era over the
past two decades thanks to the wide use of mathematical models and
computational approaches. A booming of computational biology, which
sheerly was a theoretician's fantasy twenty years ago, has become a
reality. Obsession with computational biology and theoretical
approaches is evidenced in articles hailing the arrival of what are
va- ously called quantitative biology, bioinformatics, theoretical
biology, and systems biology. New technologies and data resources
in genetics, such as the International HapMap project, enable
large-scale studies, such as genome-wide association st- ies, which
could potentially identify most common genetic variants as well as
rare variants of the human DNA that may alter individual's
susceptibility to disease and the response to medical treatment.
Meanwhile the multi-electrode recording from behaving animals makes
it feasible to control the animal mental activity, which could
potentially lead to the development of useful brain-machine
interfaces. - bracing the sheer volume of genetic, genomic, and
other type of data, an essential approach is, ?rst of all, to avoid
drowning the true signal in the data. It has been witnessed that
theoretical approach to biology has emerged as a powerful and st-
ulating research paradigm in biological studies, which in turn
leads to a new - search paradigm in mathematics, physics, and
computer science and moves forward with the interplays among
experimental studies and outcomes, simulation studies, and
theoretical investigations.
Age-Period-Cohort analysis has a wide range of applications, from
chronic disease incidence and mortality data in public health and
epidemiology, to many social events (birth, death, marriage, etc)
in social sciences and demography, and most recently investment,
healthcare and pension contribution in economics and finance.
Although APC analysis has been studied for the past 40 years and a
lot of methods have been developed, the identification problem has
been a major hurdle in analyzing APC data, where the regression
model has multiple estimators, leading to indetermination of
parameters and temporal trends. A Practical Guide to Age-Period
Cohort Analysis: The Identification Problem and Beyond provides
practitioners a guide to using APC models as well as offers
graduate students and researchers an overview of the current
methods for APC analysis while clarifying the confusion of the
identification problem by explaining why some methods address the
problem well while others do not. Features * Gives a comprehensive
and in-depth review of models and methods in APC analysis. *
Provides an in-depth explanation of the identification problem and
statistical approaches to addressing the problem and clarifying the
confusion. * Utilizes real data sets to illustrate different data
issues that have not been addressed in the literature, including
unequal intervals in age and period groups, etc. Contains
step-by-step modeling instruction and R programs to demonstrate how
to conduct APC analysis and how to conduct prediction for the
future Reflects the most recent development in APC modeling and
analysis including the intrinsic estimator Wenjiang Fu is a
professor of statistics at the University of Houston. Professor
Fu's research interests include modeling big data, applied
statistics research in health and human genome studies, and
analysis of complex economic and social science data.
Biological and biomedical studies have entered a new era over the
past two decades thanks to the wide use of mathematical models and
computational approaches. A booming of computational biology, which
sheerly was a theoretician's fantasy twenty years ago, has become a
reality. Obsession with computational biology and theoretical
approaches is evidenced in articles hailing the arrival of what are
va- ously called quantitative biology, bioinformatics, theoretical
biology, and systems biology. New technologies and data resources
in genetics, such as the International HapMap project, enable
large-scale studies, such as genome-wide association st- ies, which
could potentially identify most common genetic variants as well as
rare variants of the human DNA that may alter individual's
susceptibility to disease and the response to medical treatment.
Meanwhile the multi-electrode recording from behaving animals makes
it feasible to control the animal mental activity, which could
potentially lead to the development of useful brain-machine
interfaces. - bracing the sheer volume of genetic, genomic, and
other type of data, an essential approach is, ?rst of all, to avoid
drowning the true signal in the data. It has been witnessed that
theoretical approach to biology has emerged as a powerful and st-
ulating research paradigm in biological studies, which in turn
leads to a new - search paradigm in mathematics, physics, and
computer science and moves forward with the interplays among
experimental studies and outcomes, simulation studies, and
theoretical investigations.
Age-Period-Cohort analysis has a wide range of applications, from
chronic disease incidence and mortality data in public health and
epidemiology, to many social events (birth, death, marriage, etc)
in social sciences and demography, and most recently investment,
healthcare and pension contribution in economics and finance.
Although APC analysis has been studied for the past 40 years and a
lot of methods have been developed, the identification problem has
been a major hurdle in analyzing APC data, where the regression
model has multiple estimators, leading to indetermination of
parameters and temporal trends. A Practical Guide to Age-Period
Cohort Analysis: The Identification Problem and Beyond provides
practitioners a guide to using APC models as well as offers
graduate students and researchers an overview of the current
methods for APC analysis while clarifying the confusion of the
identification problem by explaining why some methods address the
problem well while others do not. Features * Gives a comprehensive
and in-depth review of models and methods in APC analysis. *
Provides an in-depth explanation of the identification problem and
statistical approaches to addressing the problem and clarifying the
confusion. * Utilizes real data sets to illustrate different data
issues that have not been addressed in the literature, including
unequal intervals in age and period groups, etc. Contains
step-by-step modeling instruction and R programs to demonstrate how
to conduct APC analysis and how to conduct prediction for the
future Reflects the most recent development in APC modeling and
analysis including the intrinsic estimator Wenjiang Fu is a
professor of statistics at the University of Houston. Professor
Fu's research interests include modeling big data, applied
statistics research in health and human genome studies, and
analysis of complex economic and social science data.
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