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Titolo:
On the linear transformation model for censored data
Autore:
Fine, JP; Ying, Z; Wei, LJ;
Indirizzi:
Harvard Univ, Dept Biostat, Boston, MA 02115 USA Harvard Univ Boston MA USA 02115 Univ, Dept Biostat, Boston, MA 02115 USA Rutgers Univ, Dept Stat, Piscataway, NJ 08855 USA Rutgers Univ PiscatawayNJ USA 08855 Dept Stat, Piscataway, NJ 08855 USA
Titolo Testata:
BIOMETRIKA
fascicolo: 4, volume: 85, anno: 1998,
pagine: 980 - 986
SICI:
0006-3444(199812)85:4<980:OTLTMF>2.0.ZU;2-5
Fonte:
ISI
Lingua:
ENG
Soggetto:
REGRESSION;
Keywords:
gaussian process; proportional hazards model; proportional odds model; weighted least squares;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Life Sciences
Physical, Chemical & Earth Sciences
Citazioni:
13
Recensione:
Indirizzi per estratti:
Indirizzo: Fine, JP Harvard Univ, Dept Biostat, Boston, MA 02115 USA Harvard Univ Boston MA USA 02115 t Biostat, Boston, MA 02115 USA
Citazione:
J.P. Fine et al., "On the linear transformation model for censored data", BIOMETRIKA, 85(4), 1998, pp. 980-986

Abstract

Recently Cheng, Wei & Ying (1995, 1997) proposed a class of estimation procedures for semiparametric linear transformation models with censored observations. When the support of the censoring variable is shorter than that ofthe failure time, the estimators are asymptotically biased. In this paper,we present a simple modification of Cheng's estimation procedures for the regression parameters. Through extensive numerical studies with practical sample sizes, we find that the new proposals perform well, but the original interval estimators may not have correct coverage probabilities when censoring is heavy. Prediction procedures for the survival probabilities of future subjects are also modified accordingly.

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Documento generato il 24/09/20 alle ore 02:57:01