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Statistical Inference via Convex Optimization (Princeton Series in Applied Mathematics Book 65) by Juditsky, Anatoli; Nemirovskiĭ, Arkadiĭ Semenovich is a nonfiction available to read on EtoBox.

What is Statistical Inference via Convex Optimization (Princeton Series in Applied Mathematics Book 65) about?

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 effici

Who reads Statistical Inference via Convex Optimization (Princeton Series in Applied Mathematics Book 65)?

Author

Juditsky, Anatoli; Nemirovskiĭ, Arkadiĭ Semenovich

Publisher

Princeton University Press

Published

2020

Language

EN

ISBN

9780691197296

Category

nonfiction

Subjects

Mathematics, Stem