Title

On Algorithms For The Nonparametric Maximum Likelihood Estimator Of The Failure Function With Censored Data

Keywords

Double censoring; EM algorithm; Gradient projection algorithm; Interval censoring; Iterative convex minorant algorithm; Rosen method

Abstract

In this article, we study algorithms for computing the nonparametric maximum likelihood estimator (NPMLE) of the failure function with two types of censored data: doubly censored data and (type 2) interval-censored data. We consider two projection methods, namely the iterative convex minorant algorithm (ICM) and a generalization of the Rosen algorithm (GR) and compare these methods to the well-known EM algorithm. The comparison conducted via simulation studies shows that the hybrid algorithms that alternately use the EM and OR for doubly censored data or, alternately, use the EM and ICM for (type 2) interval-censored data appear to be much more efficient than the EM, especially in large sample situation.

Publication Date

3-1-2004

Publication Title

Journal of Computational and Graphical Statistics

Volume

13

Issue

1

Number of Pages

123-140

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1198/1061860043038

Socpus ID

1842434519 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/1842434519

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