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Statistical Inference via Convex Optimization (Hardcover)
Loot Price: R2,072
Discovery Miles 20 720
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Statistical Inference via Convex Optimization (Hardcover)
Series: Princeton Series in Applied Mathematics
Expected to ship within 12 - 17 working days
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Total price: R2,092
Discovery Miles: 20 920
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This authoritative book draws on the latest research to explore the
interplay of high-dimensional statistics with optimization. Through
an accessible analysis of fundamental problems of hypothesis
testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski
show how convex optimization theory can be used to devise and
analyze near-optimal statistical inferences. Statistical Inference
via Convex Optimization is an essential resource for optimization
specialists who are new to statistics and its applications, and for
data scientists who want to improve their optimization methods.
Juditsky and Nemirovski provide the first systematic treatment of
the statistical techniques that have arisen from advances in the
theory of optimization. They focus on four well-known statistical
problems-sparse recovery, hypothesis testing, and recovery from
indirect observations of both signals and functions of
signals-demonstrating how they can be solved more efficiently as
convex optimization problems. The emphasis throughout is on
achieving the best possible statistical performance. The
construction of inference routines and the quantification of their
statistical performance are given by efficient computation rather
than by analytical derivation typical of more conventional
statistical approaches. In addition to being computation-friendly,
the methods described in this book enable practitioners to handle
numerous situations too difficult for closed analytical form
analysis, such as composite hypothesis testing and signal recovery
in inverse problems. Statistical Inference via Convex Optimization
features exercises with solutions along with extensive appendixes,
making it ideal for use as a graduate text.
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