Nonparametric confidence intervals for monotone functions

DOI10.1214/15-AOS1335zbMath1323.62040arXiv1407.3491OpenAlexW1863172740MaRDI QIDQ888502

Piet Groeneboom, Geurt Jongbloed

Publication date: 30 October 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1407.3491

zbMATH Keywords

bootstrapconfidence intervalsMLELR testisotonic estimatesmoothed MLE

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Censored data models (62N01) Nonparametric tolerance and confidence regions (62G15)

Related Items

Moderate deviations and nonparametric inference for monotone functions, Penalized nonparametric likelihood-based inference for current status data model, Nonparametric estimation and inference under shape restrictions, Isotonized smooth estimators of a monotone baseline hazard in the Cox model, Smooth estimation of a monotone hazard and a monotone density under random censoring, Nonparametric Maximum Likelihood Estimation Under a Likelihood Ratio Order, Coverage of credible intervals in Bayesian multivariate isotonic regression, A unified study of nonparametric inference for monotone functions, Some developments in the theory of shape constrained inference, Limit theory in monotone function estimation, Nonparametric shape-restricted regression, Bayesian estimation of a decreasing density, Concave regression: value-constrained estimation and likelihood ratio-based inference, Nonparametric Bayesian estimation of a concave distribution function with mixed interval censored data, Limit distribution theory for block estimators in multiple isotonic regression, Smoothed Isotonic Estimators of a Monotone Baseline Hazard in the Cox Model, Nonparametric inference under a monotone hazard ratio order, A review of uncertainty quantification for density estimation, Confidence intervals for multiple isotonic regression and other monotone models, Inference for the mode of a log-concave density, Confidence Intervals for the Current Status Model

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