Over the last few years, significant developments have been taking
place in high-dimensional data analysis, driven primarily by a wide
range of applications in many fields such as genomics and signal
processing. In particular, substantial advances have been made in
the areas of feature selection, covariance estimation,
classification and regression. This book intends to examine
important issues arising from high-dimensional data analysis to
explore key ideas for statistical inference and prediction.
It is structured around topics on multiple hypothesis testing,
feature selection, regression, classification, dimension reduction,
as well as applications in survival analysis and biomedical
research.
The book will appeal to graduate students and new researchers
interested in the plethora of opportunities available in
high-dimensional data analysis.
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