Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-016-9705-7
Accepted author manuscript, 337 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -