Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data
| Autor: | Laurent Bordes, Didier Chauveau |
|---|---|
| Přispěvatelé: | Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), Mathématiques - Analyse, Probabilités, Modélisation - Orléans (MAPMO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Finite mixture Computer science 02 engineering and technology Stochastic EM algorithm 01 natural sciences 010104 statistics & probability [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Expectation–maximization algorithm 0202 electrical engineering electronic engineering information engineering Semiparametric regression 0101 mathematics Parametric statistics 020208 electrical & electronic engineering Nonparametric statistics 16. Peace & justice Missing data Mixture model Reliability Semiparametric model Computational Mathematics Survival data Survival function Semi-parametric mixtures Censored data Statistics Probability and Uncertainty Algorithm |
| Zdroj: | Computational Statistics Computational Statistics, Springer Verlag, 2016, 31 (4), pp.1513-1538. ⟨10.1007/s00180-016-0661-7⟩ |
| ISSN: | 0943-4062 1613-9658 |
| DOI: | 10.1007/s00180-016-0661-7⟩ |
| Popis: | International audience; Mixture models in reliability bring a useful compromise between parametric and nonparametric models, when several failure modes are suspected. The classical methods for estimation in mixture models rarely handle the additional difficulty coming from the fact that lifetime data are often censored, in a deterministic or random way. We present in this paper several iterative methods based on EM and Stochastic EM methodologies, that allow us to estimate parametric or semiparametric mixture models for randomly right censored lifetime data, provided they are identifiable. We consider different levels of completion for the (incomplete) observed data, and provide genuine or EM-like algorithms for several situations. In particular, we show that simulating the missing data coming from the mixture allows to plug a standard R package for survival data analysis in an EM algorithm's M-step. Moreover, in censored semiparametric situations, a stochastic step is the only practical solution allowing computation of nonparametric estimates of the unknown survival function. The effectiveness of the new proposed algorithms are demonstrated in simulation studies and an actual dataset example from aeronautic industry. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink