Big And Complex Data Analysis: Methodologies And Applications (contributions To Statistics)
by S. Ejaz Ahmed /
2017 / English / PDF
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This volume conveys some of the surprises, puzzles and success
stories in high-dimensional and complex data analysis and related
fields. Its peer-reviewed contributions showcase recent advances
in variable selection, estimation and prediction strategies for a
host of useful models, as well as essential new developments in
the field.
This volume conveys some of the surprises, puzzles and success
stories in high-dimensional and complex data analysis and related
fields. Its peer-reviewed contributions showcase recent advances
in variable selection, estimation and prediction strategies for a
host of useful models, as well as essential new developments in
the field.
The continued and rapid advancement of modern technology now
allows scientists to collect data of increasingly unprecedented
size and complexity. Examples include epigenomic data, genomic
data, proteomic data, high-resolution image data, high-frequency
financial data, functional and longitudinal data, and network
data. Simultaneous variable selection and estimation is one of
the key statistical problems involved in analyzing such big and
complex data.
The continued and rapid advancement of modern technology now
allows scientists to collect data of increasingly unprecedented
size and complexity. Examples include epigenomic data, genomic
data, proteomic data, high-resolution image data, high-frequency
financial data, functional and longitudinal data, and network
data. Simultaneous variable selection and estimation is one of
the key statistical problems involved in analyzing such big and
complex data.
The purpose of this book is to stimulate research and foster
interaction between researchers in the area of high-dimensional
data analysis. More concretely, its goals are to: 1) highlight
and expand the breadth of existing methods in big data and
high-dimensional data analysis and their potential for the
advancement of both the mathematical and statistical sciences; 2)
identify important directions for future research in the theory
of regularization methods, in algorithmic development, and in
methodologies for different application areas; and 3) facilitate
collaboration between theoretical and subject-specific
researchers.
The purpose of this book is to stimulate research and foster
interaction between researchers in the area of high-dimensional
data analysis. More concretely, its goals are to: 1) highlight
and expand the breadth of existing methods in big data and
high-dimensional data analysis and their potential for the
advancement of both the mathematical and statistical sciences; 2)
identify important directions for future research in the theory
of regularization methods, in algorithmic development, and in
methodologies for different application areas; and 3) facilitate
collaboration between theoretical and subject-specific
researchers.