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Handbook of Big Data provides a state-of-the-art overview of the
analysis of large-scale datasets. Featuring contributions from
well-known experts in statistics and computer science, this
handbook presents a carefully curated collection of techniques from
both industry and academia. Thus, the text instills a working
understanding of key statistical and computing ideas that can be
readily applied in research and practice. Offering balanced
coverage of methodology, theory, and applications, this handbook:
Describes modern, scalable approaches for analyzing increasingly
large datasets Defines the underlying concepts of the available
analytical tools and techniques Details intercommunity advances in
computational statistics and machine learning Handbook of Big Data
also identifies areas in need of further development, encouraging
greater communication and collaboration between researchers in big
data sub-specialties such as genomics, computational biology, and
finance.
This book features research contributions from The Abel Symposium
on Statistical Analysis for High Dimensional Data, held in Nyvagar,
Lofoten, Norway, in May 2014. The focus of the symposium was on
statistical and machine learning methodologies specifically
developed for inference in "big data" situations, with particular
reference to genomic applications. The contributors, who are among
the most prominent researchers on the theory of statistics for high
dimensional inference, present new theories and methods, as well as
challenging applications and computational solutions. Specific
themes include, among others, variable selection and screening,
penalised regression, sparsity, thresholding, low dimensional
structures, computational challenges, non-convex situations,
learning graphical models, sparse covariance and precision
matrices, semi- and non-parametric formulations, multiple testing,
classification, factor models, clustering, and preselection.
Highlighting cutting-edge research and casting light on future
research directions, the contributions will benefit graduate
students and researchers in computational biology, statistics and
the machine learning community.
This book features research contributions from The Abel Symposium
on Statistical Analysis for High Dimensional Data, held in Nyvagar,
Lofoten, Norway, in May 2014. The focus of the symposium was on
statistical and machine learning methodologies specifically
developed for inference in "big data" situations, with particular
reference to genomic applications. The contributors, who are among
the most prominent researchers on the theory of statistics for high
dimensional inference, present new theories and methods, as well as
challenging applications and computational solutions. Specific
themes include, among others, variable selection and screening,
penalised regression, sparsity, thresholding, low dimensional
structures, computational challenges, non-convex situations,
learning graphical models, sparse covariance and precision
matrices, semi- and non-parametric formulations, multiple testing,
classification, factor models, clustering, and preselection.
Highlighting cutting-edge research and casting light on future
research directions, the contributions will benefit graduate
students and researchers in computational biology, statistics and
the machine learning community.
Modern statistics deals with large and complex data sets, and
consequently with models containing a large number of parameters.
This book presents a detailed account of recently developed
approaches, including the Lasso and versions of it for various
models, boosting methods, undirected graphical modeling, and
procedures controlling false positive selections. A special
characteristic of the book is that it contains comprehensive
mathematical theory on high-dimensional statistics combined with
methodology, algorithms and illustrations with real data examples.
This in-depth approach highlights the methods' great potential and
practical applicability in a variety of settings. As such, it is a
valuable resource for researchers, graduate students and experts in
statistics, applied mathematics and computer science.
Handbook of Big Data provides a state-of-the-art overview of the
analysis of large-scale datasets. Featuring contributions from
well-known experts in statistics and computer science, this
handbook presents a carefully curated collection of techniques from
both industry and academia. Thus, the text instills a working
understanding of key statistical and computing ideas that can be
readily applied in research and practice. Offering balanced
coverage of methodology, theory, and applications, this handbook:
Describes modern, scalable approaches for analyzing increasingly
large datasets Defines the underlying concepts of the available
analytical tools and techniques Details intercommunity advances in
computational statistics and machine learning Handbook of Big Data
also identifies areas in need of further development, encouraging
greater communication and collaboration between researchers in big
data sub-specialties such as genomics, computational biology, and
finance.
Modern statistics deals with large and complex data sets, and
consequently with models containing a large number of parameters.
This book presents a detailed account of recently developed
approaches, including the Lasso and versions of it for various
models, boosting methods, undirected graphical modeling, and
procedures controlling false positive selections. A special
characteristic of the book is that it contains comprehensive
mathematical theory on high-dimensional statistics combined with
methodology, algorithms and illustrations with real data examples.
This in-depth approach highlights the methods' great potential and
practical applicability in a variety of settings. As such, it is a
valuable resource for researchers, graduate students and experts in
statistics, applied mathematics and computer science.
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