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Dynamic Mixed Models for Familial Longitudinal Data (Paperback, 2011 ed.)
Loot Price: R2,852
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Dynamic Mixed Models for Familial Longitudinal Data (Paperback, 2011 ed.)
Series: Springer Series in Statistics
Expected to ship within 10 - 15 working days
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This book provides a theoretical foundation for the analysis of
discrete data such as count and binary data in the longitudinal
setup. Unlike the existing books, this book uses a class of
auto-correlation structures to model the longitudinal correlations
for the repeated discrete data that accommodates all possible
Gaussian type auto-correlation models as special cases including
the equi-correlation models. This new dynamic modelling approach is
utilized to develop theoretically sound inference techniques such
as the generalized quasi-likelihood (GQL) technique for consistent
and efficient estimation of the underlying regression effects
involved in the model, whereas the existing 'working' correlations
based GEE (generalized estimating equations) approach has serious
theoretical limitations both for consistent and efficient
estimation, and the existing random effects based correlations
approach is not suitable to model the longitudinal correlations.
The book has exploited the random effects carefully only to model
the correlations of the familial data. Subsequently, this book has
modelled the correlations of the longitudinal data collected from
the members of a large number of independent families by using the
class of auto-correlation structures conditional on the random
effects. The book also provides models and inferences for discrete
longitudinal data in the adaptive clinical trial set up. The book
is mathematically rigorous and provides details for the development
of estimation approaches under selected familial and longitudinal
models. Further, while the book provides special cares for
mathematics behind the correlation models, it also presents the
illustrations of the statistical analysis of various real life
data. This book will be of interest to the researchers including
graduate students in biostatistics and econometrics, among other
applied statistics research areas. Brajendra Sutradhar is a
University Research Professor at Memorial University in St. John's,
Canada. He is an elected member of the International Statistical
Institute and a fellow of the American Statistical Association. He
has published about 110 papers in statistics journals in the area
of multivariate analysis, time series analysis including
forecasting, sampling, survival analysis for correlated failure
times, robust inferences in generalized linear mixed models with
outliers, and generalized linear longitudinal mixed models with
bio-statistical and econometric applications. He has served as an
associate editor for six years for Canadian Journal of Statistics
and for four years for the Journal of Environmental and Ecological
Statistics. He has served for 3 years as a member of the advisory
committee on statistical methods in Statistics Canada. Professor
Sutradhar was awarded 2007 distinguished service award of
Statistics Society of Canada for his many years of services to the
society including his special services for society's annual
meetings.
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