نتایج جستجو برای: censoring
تعداد نتایج: 4582 فیلتر نتایج به سال:
In this paper we consider general Hadamard differentiable functionals φ(ΛR,ΛT ) of the cumulative hazard functions of two samples of randomly right censored data, which can be used for the nonparametric assessment of noninferiority. We prove the consistency of various bootstrap procedures as suggested in Freitag et al. [1] for the practical implementation of tests for this problem.
This letter evaluates the performance of auxiliary regression-based specification tests for parametric duration models estimated with censored data. The test using asymptotic critical values has poor size. Bootstrapping corrects the size problem but results in a biased power curve.
This paper presents a Bayesian nonparametric approach to survival analysis based on arbitrarly right censored data. The first aim will be to show that the neutral to the right process is the natural prior to use in this context. Secondly, the properties of a particular neutral to the right process, the beta-Stacy process are examined. Finally, the connections between some Bayesian bootstraps an...
HSING-VI CHANG. Testing Overdispersion in Data With Censoring. (Under the direction of Chirayath M. Suchindran.) The term overdispersion refers to the situation that the variance of the outcome exceeds the nominal variance. Overdispersion in general has two effects. The first effect is that summary statistics have a larger variance than anticipated under the simple model. The second is a possib...
We define a new class of models for multivariate survival data, in continuous time, based on a number of cumulative hazard functions, along the lines of our family of models for correlated survival data in discrete time (Gross and Huber, 2000, 2002). This family is an alternative to frailty and copula models. We establish some properties of our family and compare it to Clayton’s and Marshall-Ol...
Methods for detecting influential observations for the Weibull model fit to censored data are discussed. These methods include: one-step deletion diagnostics, influence functions and curvature diagnostics. Results indicate that the curvature diagnostics may be helpful in detecting masking.
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