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Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification

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Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification. / Titman, Andrew Charles.
In: Statistics and Computing, Vol. 27, No. 6, 11.2017, p. 1585-1593.

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Titman AC. Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification. Statistics and Computing. 2017 Nov;27(6):1585-1593. Epub 2016 Sept 29. doi: 10.1007/s11222-016-9705-7

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@article{6a33e1137330450eb7e332ac32a3ffd3,
title = "Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification",
abstract = "The paper considers non-parametric maximum likelihood estimation of the failure time distribution for interval censored data subject to misclassification. Such data can arise from two types of observation scheme; either where observations continue until the first positive test result or where tests continue regardless of the test results. In the former case, the misclassification probabilities must be known, whereas in the latter case joint estimation of the event-time distribution and misclassification probabilities is possible. The regions for which the maximum likelihood estimate can only have support are derived. Algorithms for computing the maximum likelihood estimate are investigated and it is shown that algorithms appropriate for computing non-parametric mixing distributions perform better than an iterative convex minorant algorithm in terms of time to absolute convergence. A profile likelihood approach is proposed for joint estimation. The methods are illustrated on a data set relating to the onset of cardiac allograft vasculopathy in post-heart-transplantation patients.",
keywords = "Interval-censored data, NPMLE , Misclassification, Directional derivatives",
author = "Titman, {Andrew Charles}",
note = "This is the peer reviewed version of the following article: Titman, A.C. (2016) Non-parametric maximum likelihood estimation of interval censored failure time data subject to misclassification Statistics and Computing. DOI:10.1007/s11222-016-9705-7, The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-016-9705-7",
year = "2017",
month = nov,
doi = "10.1007/s11222-016-9705-7",
language = "English",
volume = "27",
pages = "1585--1593",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "6",

}

RIS

TY - JOUR

T1 - Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification

AU - Titman, Andrew Charles

N1 - This is the peer reviewed version of the following article: Titman, A.C. (2016) Non-parametric maximum likelihood estimation of interval censored failure time data subject to misclassification Statistics and Computing. DOI:10.1007/s11222-016-9705-7, The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-016-9705-7

PY - 2017/11

Y1 - 2017/11

N2 - The paper considers non-parametric maximum likelihood estimation of the failure time distribution for interval censored data subject to misclassification. Such data can arise from two types of observation scheme; either where observations continue until the first positive test result or where tests continue regardless of the test results. In the former case, the misclassification probabilities must be known, whereas in the latter case joint estimation of the event-time distribution and misclassification probabilities is possible. The regions for which the maximum likelihood estimate can only have support are derived. Algorithms for computing the maximum likelihood estimate are investigated and it is shown that algorithms appropriate for computing non-parametric mixing distributions perform better than an iterative convex minorant algorithm in terms of time to absolute convergence. A profile likelihood approach is proposed for joint estimation. The methods are illustrated on a data set relating to the onset of cardiac allograft vasculopathy in post-heart-transplantation patients.

AB - The paper considers non-parametric maximum likelihood estimation of the failure time distribution for interval censored data subject to misclassification. Such data can arise from two types of observation scheme; either where observations continue until the first positive test result or where tests continue regardless of the test results. In the former case, the misclassification probabilities must be known, whereas in the latter case joint estimation of the event-time distribution and misclassification probabilities is possible. The regions for which the maximum likelihood estimate can only have support are derived. Algorithms for computing the maximum likelihood estimate are investigated and it is shown that algorithms appropriate for computing non-parametric mixing distributions perform better than an iterative convex minorant algorithm in terms of time to absolute convergence. A profile likelihood approach is proposed for joint estimation. The methods are illustrated on a data set relating to the onset of cardiac allograft vasculopathy in post-heart-transplantation patients.

KW - Interval-censored data

KW - NPMLE

KW - Misclassification

KW - Directional derivatives

U2 - 10.1007/s11222-016-9705-7

DO - 10.1007/s11222-016-9705-7

M3 - Journal article

VL - 27

SP - 1585

EP - 1593

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 6

ER -