全部文献期刊会议图书|学者科研项目
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作者:Alan E. Gelfand , Athanasios Kottas
来源:[J].Scandinavian Journal of Statistics(IF 1.169), 2003, Vol.30 (4), pp.651-665
摘要:... With survival data there is often interest not only in the survival time distribution but also in the residual survival time distribution. In fact, regression models to explain residual survival time might be desired. Building upon recent work of Kottas & Gelfand [ J. Amer....
作者:Salvatore Calabrese , Amilcare Porporato
来源:[J].Water Resources Research(IF 3.149), 2017, Vol.53 (1), pp.110-126
摘要:... We first extend the theory to linear systems with multiple outflows, including the relationship between age distribution at death and survival time distribution at birth. We further show that for each outflow there is a survival time distribution at birth, which normaliz...
作者:Olayidé Boussari , Gaëlle Romain , Laurent Remontet ...
来源:[J].Cancer Epidemiology(IF 2.232), 2018, Vol.53, pp.72-80
摘要:Abstract(#br)Background(#br)Cure models have been adapted to net survival context to provide important indicators from population-based cancer data, such as the cure fraction and the time-to-cure. However existing methods for computing time-to-cure suffer from some limitations.(#...
作者:R.E. Lillo
来源:[J].Reliability Engineering and System Safety(IF 1.901), 2000, Vol.67 (2), pp.129-133
摘要:Abstract(#br)This work is focused on proving implications between criteria for ageing of a system with a well-defined survival time distribution. Specifically, two reliability measures, the mean residual lifetime (MRL) and the hazard rate (HR) are considered. The current article ...
作者:Malwane M. Ananda , Rohan J. Dalpatadu , Ashok K. Singh
来源:[J].Applied Mathematics and Computation(IF 1.349), 1996, Vol.75 (2), pp.167-177
摘要:Abstract(#br)The two-parameter Gompertz model is a commonly used survival time distribution in actuarial science and reliability and life testing. The estimation of the parameters of this model is numerically involved. We consider the estimation problem in a Bayesian framework an...
作者:Zahra Mansourvar , Torben Martinussen , Thomas H. Scheike
来源:[J].Scandinavian Journal of Statistics(IF 1.169), 2016, Vol.43 (2), pp.487-504
摘要:Abstract(#br)The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an ad...
作者:Loïc Turban , Jean-Yves Fortin
来源:[J].Journal of Physics A: Mathematical and Theoretical, 2018, Vol.51 (14)
摘要:... Exact expressions for the particle density distribution at a given time and survival time distribution for a given number of particles are obtained. In particular, we show that the time needed to reach a finite number of surviving particles (vanishing density in the scaling l...
作者:MOULINATH BANERJEE , JON A. WELLNER
来源:[J].Scandinavian Journal of Statistics(IF 1.169), 2005, Vol.32 (3), pp.405-424
摘要:... The likelihood ratio statistic for testing pointwise hypotheses about the survival time distribution in the current status model can be inverted to yield confidence intervals (CIs). One advantage of this procedure is that CIs can be formed without estimating the unknown para...
作者:Andrew G. Chapple , Peter F. Thall
来源:[J].Biometrics(IF 1.412), 2019, Vol.75 (2), pp.371-381
摘要:... Phase I is based on toxicity to determine a “maximum tolerable dose” (MTD) of A , phase II is conducted to decide whether A at the MTD is promising in terms of response probability, and if so a large randomized phase III trial is conducted to compare A to a control treatment, C , usually based on survival time...
作者:Azimeh N.V. Dehkordi , Alireza Kamali‐Asl , Ning Wen ...
来源:[J].NMR in Biomedicine(IF 3.446), 2017, Vol.30 (9), pp.n/a-n/a
摘要:This pilot study investigates the construction of an Adaptive Neuro‐Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selecti...

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