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

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...

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Veröffentlicht in:Statistics and computing Jg. 27; H. 6; S. 1585 - 1593
1. Verfasser: Titman, Andrew C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2017
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computation
Economic models
Estimating techniques
Heart
Iterative methods
Mathematics and Statistics
Maximum likelihood estimates
Maximum likelihood estimation
Maximum likelihood method
Nonparametric statistics
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Transplantation
Interval-censored data
Misclassification
NPMLE
Directional derivatives
ISSN:0960-3174, 1573-1375
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Zusammenfassung: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.
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ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-016-9705-7