Hypothesis testing via affine detectors

DOI10.1214/16-EJS1170zbMath1345.62077arXiv1604.02576OpenAlexW3104161870WikidataQ57392868 ScholiaQ57392868MaRDI QIDQ309575

Arkadi Nemirovski, Anatoli B. Juditsky

Publication date: 7 September 2016

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1604.02576

zbMATH Keywords

hypothesis testingnonparametric testingcomposite hypothesis testingstatistical applications of convex optimization

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Minimax procedures in statistical decision theory (62C20) Convex programming (90C25) Numerical optimization and variational techniques (65K10) Markov processes: hypothesis testing (62M02)

Related Items

Change detection via affine and quadratic detectors, Estimating linear and quadratic forms via indirect observations, Hypothesis testing via Euclidean separation, Aggregating estimates by convex optimization

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Hypothesis testing by convex optimization
• Discrimination of hypotheses for Gaussian measures, and a geometrical characterization of Gaussian distribution
• A universal procedure for aggregating estimators
• Geometrizing rates of convergence. III
• Nonparametric goodness-of-fit testing under Gaussian models
• Model selection via testing: an alternative to (penalized) maximum likelihood estimators.
• Convergence of estimates under dimensionality restrictions
• On the Minimax Detection of an Inaccurately Known Signal in a White Gaussian Noise Background
• Robust tests for model selection
• A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations