The Annals of Statistics

Nonparametric confidence intervals for monotone functions

Piet Groeneboom and Geurt Jongbloed

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Abstract

We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the treatment of other models with monotone functions, and demonstrate our method with a new proof of the results of Banerjee–Wellner [Ann. Statist. 29 (2001) 1699–1731] and also by constructing confidence intervals for monotone densities, for which a theory remained be developed. For the latter model we prove that the limit distribution of the LR test under the null hypothesis is the same as in the current status model. We compare the confidence intervals, so obtained, with confidence intervals using the smoothed maximum likelihood estimator (SMLE), using bootstrap methods. The “Lagrange-modified” cusum diagrams, developed here, are an essential tool both for the computation of the restricted MLEs and for the development of the theory for the confidence intervals, based on the LR tests.

Article information

Source
Ann. Statist. Volume 43, Number 5 (2015), 2019-2054.

Dates
Received: June 2014
Revised: January 2015
First available in Project Euclid: 3 August 2015

Permanent link to this document
https://projecteuclid.org/euclid.aos/1438606852

Digital Object Identifier
doi:10.1214/15-AOS1335

Mathematical Reviews number (MathSciNet)
MR3375875

Zentralblatt MATH identifier
1323.62040

Subjects
Primary: 62G05: Estimation 62N01: Censored data models
Secondary: 62G20: Asymptotic properties

Keywords
LR test MLE confidence intervals isotonic estimate smoothed MLE bootstrap

Citation

Groeneboom, Piet; Jongbloed, Geurt. Nonparametric confidence intervals for monotone functions. Ann. Statist. 43 (2015), no. 5, 2019--2054. doi:10.1214/15-AOS1335. https://projecteuclid.org/euclid.aos/1438606852.


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