A hybrid ML-EM algorithm for calculation of maximum likelihood estimates in semiparametric shared frailty models

This paper describes a generalised hybrid ML-EM algorithm for the calculation of maximum likelihood estimates in semiparametric shared frailty models, the Cox proportional hazard models with hazard functions multiplied by a (parametric) frailty random variable. This hybrid method is much faster than...

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Veröffentlicht in:	Computational statistics & data analysis Jg. 40; H. 1; S. 173 - 187
Hauptverfasser:	Vu, Hien T.V., Knuiman, Matthew W.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 28.07.2002 Elsevier Science Elsevier
Schriftenreihe:	Computational Statistics & Data Analysis
Schlagworte:	Applications Distribution theory Exact sciences and technology Frailty variance estimates Hybrid ML-EM Linear inference, regression Mathematics Medical sciences MLE Newton–Raphson Non-parametric hazards Parametric inference Probability and statistics Sciences and techniques of general use Shared frailties Simulations Statistics Simulations Shared frailties MLE Newton–Raphson EM Frailty variance estimates Hybrid ML-EM Non-parametric hazards ML Biometrics Normal variable Statistical distribution Variance estimation Large sample Gaussian distribution Non parametric estimation Statistical simulation Variance Semiparametric model Hypothesis test Statistical test Medical science Distribution function Simulation model Newton method Random variable Hazard Likelihood function Mixed distribution Asymptotic property Hazard function Covariance analysis Asymptotic behavior Semiparametric method Variance analysis Newton Raphson method Statistical method Statistical regression Cox model Simulation Asymptotic normality Small sample Asymptotic approximation Maximum likelihood EM algorithm Small sample property
ISSN:	0167-9473, 1872-7352
Online-Zugang:	Volltext
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