Estimation of the density for censored and contaminated data
Consider a situation where one is interested in estimating the density of a survival time that is subject to random right censoring and measurement errors. This happens often in practice, like in public health (pregnancy length), medicine (duration of infection), ecology (duration of forest fire), a...
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| Published in | Stat (International Statistical Institute) Vol. 13; no. 1 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2049-1573 2049-1573 |
| DOI | 10.1002/sta4.651 |
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| Abstract | Consider a situation where one is interested in estimating the density of a survival time that is subject to random right censoring and measurement errors. This happens often in practice, like in public health (pregnancy length), medicine (duration of infection), ecology (duration of forest fire), among others. We assume a classical additive measurement error model with Gaussian noise and unknown error variance and a random right censoring scheme. Under this setup, we develop minimal conditions under which the assumed model is identifiable when no auxiliary variables or validation data are available, and we offer a flexible estimation strategy using Laguerre polynomials for the estimation of the error variance and the density of the survival time. The asymptotic normality of the proposed estimators is established, and the numerical performance of the methodology is investigated on both simulated and real data on gestational age. |
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| AbstractList | Consider a situation where one is interested in estimating the density of a survival time that is subject to random right censoring and measurement errors. This happens often in practice, like in public health (pregnancy length), medicine (duration of infection), ecology (duration of forest fire), among others. We assume a classical additive measurement error model with Gaussian noise and unknown error variance and a random right censoring scheme. Under this setup, we develop minimal conditions under which the assumed model is identifiable when no auxiliary variables or validation data are available, and we offer a flexible estimation strategy using Laguerre polynomials for the estimation of the error variance and the density of the survival time. The asymptotic normality of the proposed estimators is established, and the numerical performance of the methodology is investigated on both simulated and real data on gestational age. |
| Author | Kekeç, Elif Van Keilegom, Ingrid |
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| Cites_doi | 10.1093/humrep/det297 10.2307/1912526 10.1007/s00404-016-4153-3 10.1016/j.spl.2009.10.012 10.1016/j.annepidem.2018.08.011 10.1007/978-1-4939-6640-0 10.1002/9781119942412 10.1214/22-EJS1996 10.1201/9781420010138 10.1111/stan.12103 10.1111/biom.12960 10.1093/biostatistics/kxs053 10.1111/1467-9868.00163 10.2307/1390786 10.1007/BF01794434 10.1080/10485252.2022.2028281 10.1007/s00362-023-01443-y 10.24033/bsmf.131 10.1080/01621459.2018.1555093 10.1007/s11749-017-0548-0 10.1073/pnas.72.1.20 |
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| Notes | The research of I. Van Keilegom and E. Kekeç was supported by the European Research Council (2016‐2022, Horizon 2020 / ERC grant agreement no. 694409). In addition, I. Van Keilegom gratefully acknowledges funding from the FWO and F.R.S.‐FNRS under the Excellence of Science (EOS) programme, project ASTeRISK (grant no. 40007517). |
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| SubjectTerms | censoring density estimation gestational age identifiability Laguerre polynomials measurement error survival analysis |
| Title | Estimation of the density for censored and contaminated data |
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