Titlebook: Survival Analysis: State of the Art; John P. Klein,Prem K. Goel Book 1992 Springer Science+Business Media B.V. 1992 Estimator.Radiologiein

污點(diǎn)

Kernel Density Estimation from Record-Breaking Dataments. Here, for such record-breaking data, kernel density estimation is considered. For a single record-breaking sample, consistent estimation is not possible except in the extreme tails of the distribution. Hence, replication is required, and for m such independent record-breaking samples, the ker

誘騙

Semiparametric Estimation Of Parametric Hazard Ratesmetric ones are sometimes too biased while the nonparametric ones are sometimes too variable. There should therefore be scope for methods that somehow try to combine parametric and nonparametric features. In the present paper three semiparametric approaches to hazard rate estimation are presented. T

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Cox-Type Regression Analysis for Large Numbers of Small Groups of Correlated Failure Time Observatioure time observations, we show that the standard maximum partial likelihood estimate of the regression coefficient in the Cox model is still consistent and asymptotically normal. However, the corresponding standard variance-covariance estimate may no longer be valid due to the dependence among membe

exhilaration

從容

生銹

庇護(hù)

BUMP

Bayesian Computations in Survival Models Via the Gibbs Samplerperspective, these features combine to create difficult computational problems by seeming to require (multi-dimensional) numerical integrals over awkwardly defined regions. This paper illustrates how these apparent difficulties can be overcome, in both parametric and nonparametric settings, by the Gibbs sampler approach to Bayesian computation.

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OATH