On algorithms for the nonparametric maximum likelihood estimator of the failure function with censored data

    Authors

    Y. Zhang;M. Jamshidian

    Comments

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    Abbreviated Journal Title

    J. Comput. Graph. Stat.

    Keywords

    double censoring; EM algorithm; gradient projection algorithm; interval; censoring; iterative convex minorant algorithm; Rosen method; CONVEX MINORANT ALGORITHM; PANEL COUNT DATA; SELF-CONSISTENT; EM; ALGORITHM; COX MODEL; COMPUTATION; Statistics & Probability

    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 GR 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.

    Journal Title

    Journal of Computational and Graphical Statistics

    Volume

    13

    Issue/Number

    1

    Publication Date

    1-1-2004

    Document Type

    Article

    Language

    English

    First Page

    123

    Last Page

    140

    WOS Identifier

    WOS:000220181900008

    ISSN

    1061-8600

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