Statistical inference via convex optimization /

"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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Juditsky, Anatoli, 1962- (Autor), Nemirovskiĭ, A. S. (Arkadiĭ Semenovich) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Princeton, New Jersey : Princeton University Press, [2020]
Colección:Princeton series in applied mathematics.
Temas:
Mathematical statistics.
Mathematical optimization.
Convex functions.
Optimisation mathématique.
Fonctions convexes.
MATHEMATICS > Optimization.
All of Nonparametric Statistics.
Asymptotic Methods in Statistical Decision Theory.
Dantzig selector.
Gaussian observations.
Has'minskii.
Hellinger distance.
Ibragimov.
Introduction to Nonparametric Estimation.
Lagrange duality.
Le Cam.
N-convex function.
Statistical Estimation.
Tsybakov.
Wasserman.
bisection algorithm.
conic programming.
convex sets.
duality.
ell-1-norm minimization.
estimating functions.
lasso selector.
minimization.
saddle points.
signal plus noise.
signal-to-noise.
unobserved signal.
variable selection.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • On computational tractability
  • Sparse recovery via ℓ₁ minimization
  • Hypothesis testing
  • From hypothesis testing to estimating functionals
  • Signal recovery by linear estimation
  • Signal recovery beyond linear estimates
  • Solutions to selected exercises.

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