Minimax Euclidean separation rates for testing convex hypotheses in \(\mathbb{R}^{d}\)
From MaRDI portal
Publication:1627564
DOI10.1214/18-EJS1472zbMath1402.60126arXiv1702.03760OpenAlexW2725018157MaRDI QIDQ1627564
Maurilio Gutzeit, Gilles Blanchard, Alexandra Carpentier
Publication date: 30 November 2018
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1702.03760
Related Items (6)
Optimal rates of estimation for multi-reference alignment ⋮ Inference on the maximal rank of time-varying covariance matrices using high-frequency data ⋮ Nonasymptotic one- and two-sample tests in high dimension with unknown covariance structure ⋮ Dimension-agnostic inference using cross U-statistics ⋮ Optimal nonparametric testing of missing completely at random and its connections to compatibility ⋮ Optimal detection of the feature matching map in presence of noise and outliers
Cites Work
- Unnamed Item
- Adaptive confidence sets in \(L^2\)
- Testing composite hypotheses, Hermite polynomials and optimal estimation of a nonsmooth functional
- Minimax detection of a signal for Besov bodies and balls.
- On estimation of the \(L_r\) norm of a regression function
- Testing convex hypotheses on the mean of a Gaussian vector. Application to testing qualitative hypotheses on a regression function
- On nonparametric tests of positivity/monotonicity/convexity
- Nonparametric goodness-of-fit testing under Gaussian models
- Non-asymptotic minimax rates of testing in signal detection
- Minimax testing of a composite null hypothesis defined via a quadratic functional in the model of regression
- Testing the regularity of a smooth signal
- On estimating the perimeter using the alpha-shape
- Adaptive minimax testing in the discrete regression scheme
- On Testing a Hypothesis Which Is Close to a Simple Hypothesis
- On the Minimax Detection of an Inaccurately Known Signal in a White Gaussian Noise Background
- An alternative point of view on Lepski's method
- A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
This page was built for publication: Minimax Euclidean separation rates for testing convex hypotheses in \(\mathbb{R}^{d}\)
