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In the statistical domain, certain topics have received
considerable attention during the last decade or so, necessitated
by the growth and evolution of data and theoretical challenges.
This growth has invariably been accompanied by computational
advancement, which has presented end users as well as researchers
with the necessary opportunities to handle data and implement
modelling solutions for statistical purposes. Showcasing the
interplay among a variety of disciplines, this book offers
pioneering theoretical and applied solutions to practice-oriented
problems. As a carefully curated collection of prominent
international thought leaders, it fosters collaboration between
statisticians and biostatisticians and provides an array of thought
processes and tools to its readers. The book thereby creates an
understanding and appreciation of recent developments as well as an
implementation of these contributions within the broader framework
of both academia and industry. Computational and Methodological
Statistics and Biostatistics is composed of three main themes: *
Recent developments in theory and applications of statistical
distributions;* Recent developments in supervised and unsupervised
modelling;* Recent developments in biostatistics; and also features
programming code and accompanying algorithms to enable readers to
replicate and implement methodologies. Therefore, this monograph
provides a concise point of reference for a variety of current
trends and topics within the statistical domain. With
interdisciplinary appeal, it will be useful to researchers,
graduate students, and practitioners in statistics, biostatistics,
clinical methodology, geology, data science, and actuarial science,
amongst others.
In the statistical domain, certain topics have received
considerable attention during the last decade or so, necessitated
by the growth and evolution of data and theoretical challenges.
This growth has invariably been accompanied by computational
advancement, which has presented end users as well as researchers
with the necessary opportunities to handle data and implement
modelling solutions for statistical purposes. Showcasing the
interplay among a variety of disciplines, this book offers
pioneering theoretical and applied solutions to practice-oriented
problems. As a carefully curated collection of prominent
international thought leaders, it fosters collaboration between
statisticians and biostatisticians and provides an array of thought
processes and tools to its readers. The book thereby creates an
understanding and appreciation of recent developments as well as an
implementation of these contributions within the broader framework
of both academia and industry. Computational and Methodological
Statistics and Biostatistics is composed of three main themes: *
Recent developments in theory and applications of statistical
distributions;* Recent developments in supervised and unsupervised
modelling;* Recent developments in biostatistics; and also features
programming code and accompanying algorithms to enable readers to
replicate and implement methodologies. Therefore, this monograph
provides a concise point of reference for a variety of current
trends and topics within the statistical domain. With
interdisciplinary appeal, it will be useful to researchers,
graduate students, and practitioners in statistics, biostatistics,
clinical methodology, geology, data science, and actuarial science,
amongst others.
Multivariate statistical analysis has undergone a rich and varied
evolution during the latter half of the 20th century. Academics and
practitioners have produced much literature with diverse interests
and with varying multidisciplinary knowledge on different topics
within the multivariate domain. Due to multivariate algebra being
of sustained interest and being a continuously developing field,
its appeal breaches laterally across multiple disciplines to act as
a catalyst for contemporary advances, with its core inferential
genesis remaining in that of statistics. It is exactly this varied
evolution caused by an influx in data production, diffusion, and
understanding in scientific fields that has blurred many lines
between disciplines. The cross-pollination between statistics and
biology, engineering, medical science, computer science, and even
art, has accelerated the vast amount of questions that statistical
methodology has to answer and report on. These questions are often
multivariate in nature, hoping to elucidate uncertainty on more
than one aspect at the same time, and it is here where statistical
thinking merges mathematical design with real life interpretation
for understanding this uncertainty. Statistical advances benefit
from these algebraic inventions and expansions in the multivariate
paradigm. This contributed volume aims to usher novel research
emanating from a multivariate statistical foundation into the
spotlight, with particular significance in multidisciplinary
settings. The overarching spirit of this volume is to highlight
current trends, stimulate a focus on, and connect multidisciplinary
dots from and within multivariate statistical analysis. Guided by
these thoughts, a collection of research at the forefront of
multivariate statistical thinking is presented here which has been
authored by globally recognized subject matter experts.
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