Nonparametric confidence intervals for monotone functions. (English) Zbl 1323.62040

Summary: We study nonparametric isotonic confidence intervals for monotone functions. In [M. Banerjee and J. A. Wellner, Ann. Stat. 29, No. 6, 1699–1731 (2001; Zbl 1043.62037)], 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 [loc. cit.] 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.


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


Zbl 1043.62037
Full Text: DOI arXiv Euclid


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