|
Showing 1 - 7 of
7 matches in All Departments
This proceedings volume contains nine selected papers that were
presented in the International Symposium in Statistics, 2012 held
at Memorial University from July 16 to 18. These nine papers cover
three different areas for longitudinal data analysis, four dealing
with longitudinal data subject to measurement errors, four on
incomplete longitudinal data analysis, and the last one for
inferences for longitudinal data subject to outliers. Unlike in the
independence setup, the inferences in measurement errors, missing
values, and/or outlier models, are not adequately discussed in the
longitudinal setup. The papers in the present volume provide
details on successes and further challenges in these three areas
for longitudinal data analysis. This volume is the first outlet
with current research in three important areas in the longitudinal
setup. The nine papers presented in three parts clearly reveal the
similarities and differences in inference techniques used for three
different longitudinal setups. Because the research problems
considered in this volume are encountered in many real life studies
in biomedical, clinical, epidemiology, socioeconomic, econometrics,
and engineering fields, the volume should be useful to the
researchers including graduate students in these areas.
This is the first book in longitudinal categorical data analysis
with parametric correlation models developed based on dynamic
relationships among repeated categorical responses. This book is a
natural generalization of the longitudinal binary data analysis to
the multinomial data setup with more than two categories. Thus,
unlike the existing books on cross-sectional categorical data
analysis using log linear models, this book uses multinomial
probability models both in cross-sectional and longitudinal setups.
A theoretical foundation is provided for the analysis of univariate
multinomial responses, by developing models systematically for the
cases with no covariates as well as categorical covariates, both in
cross-sectional and longitudinal setups. In the longitudinal setup,
both stationary and non-stationary covariates are considered. These
models have also been extended to the bivariate multinomial setup
along with suitable covariates. For the inferences, the book uses
the generalized quasi-likelihood as well as the exact likelihood
approaches.The book is technically rigorous, and, it also presents
illustrations of the statistical analysis of various real life data
involving univariate multinomial responses both in cross-sectional
and longitudinal setups. This book is written mainly for the
graduate students and researchers in statistics and social
sciences, among other applied statistics research areas. However,
the rest of the book, specifically the chapters from 1 to 3, may
also be used for a senior undergraduate course in statistics.
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.
This is the first book in longitudinal categorical data analysis
with parametric correlation models developed based on dynamic
relationships among repeated categorical responses. This book is a
natural generalization of the longitudinal binary data analysis to
the multinomial data setup with more than two categories. Thus,
unlike the existing books on cross-sectional categorical data
analysis using log linear models, this book uses multinomial
probability models both in cross-sectional and longitudinal setups.
A theoretical foundation is provided for the analysis of univariate
multinomial responses, by developing models systematically for the
cases with no covariates as well as categorical covariates, both in
cross-sectional and longitudinal setups. In the longitudinal setup,
both stationary and non-stationary covariates are considered. These
models have also been extended to the bivariate multinomial setup
along with suitable covariates. For the inferences, the book uses
the generalized quasi-likelihood as well as the exact likelihood
approaches.The book is technically rigorous, and, it also presents
illustrations of the statistical analysis of various real life data
involving univariate multinomial responses both in cross-sectional
and longitudinal setups. This book is written mainly for the
graduate students and researchers in statistics and social
sciences, among other applied statistics research areas. However,
the rest of the book, specifically the chapters from 1 to 3, may
also be used for a senior undergraduate course in statistics.
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.
This proceedings volume contains eight selected papers that were
presented in the International Symposium in Statistics (ISS) 2015
On Advances in Parametric and Semi-parametric Analysis of
Multivariate, Time Series, Spatial-temporal, and
Familial-longitudinal Data, held in St. John's, Canada from July 6
to 8, 2015. The main objective of the ISS-2015 was the discussion
on advances and challenges in parametric and semi-parametric
analysis for correlated data in both continuous and discrete
setups. Thus, as a reflection of the theme of the symposium, the
eight papers of this proceedings volume are presented in four
parts. Part I is comprised of papers examining Elliptical t
Distribution Theory. In Part II, the papers cover spatial and
temporal data analysis. Part III is focused on longitudinal
multinomial models in parametric and semi-parametric setups.
Finally Part IV concludes with a paper on the inferences for
longitudinal data subject to a challenge of important covariates
selection from a set of large number of covariates available for
the individuals in the study.
This proceedings volume contains nine selected papers that were
presented in the International Symposium in Statistics, 2012 held
at Memorial University from July 16 to 18. These nine papers cover
three different areas for longitudinal data analysis, four dealing
with longitudinal data subject to measurement errors, four on
incomplete longitudinal data analysis, and the last one for
inferences for longitudinal data subject to outliers. Unlike in the
independence setup, the inferences in measurement errors, missing
values, and/or outlier models, are not adequately discussed in the
longitudinal setup. The papers in the present volume provide
details on successes and further challenges in these three areas
for longitudinal data analysis. This volume is the first outlet
with current research in three important areas in the longitudinal
setup. The nine papers presented in three parts clearly reveal the
similarities and differences in inference techniques used for three
different longitudinal setups. Because the research problems
considered in this volume are encountered in many real life studies
in biomedical, clinical, epidemiology, socioeconomic, econometrics,
and engineering fields, the volume should be useful to the
researchers including graduate students in these areas.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|