Name
Abstract | ||
|---|---|---|
In life testing experiments, the skewed distributions like log-normal, Weibull, gamma and generalized gamma are the most suitable models for recording the failure time measurements. In this paper, a generalized version of log-normal distribution is proposed and its goodness-of-fit for a randomly censored data set representing the remission times of bladder cancer patients has been demonstrated and compared with other lifetime models considered in the literature. The P-P plots of Kaplan-Meier estimator against the survival functions of the considered models are used to show the goodness-of-fit. A simulation study is also performed to estimate the parameters in both the classical and Bayesian setups. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s00180-011-0233-9 | Computational Statistics |
Keywords | DocType | Volume |
lifetime model,generalized gamma,life testing experiment,failure time measurement,generalized log-normal distribution,bladder cancer patient,maximum likelihood estimator · bayes estimator · squared error loss function · kaplan-meier estimator · markov chain monte carlo · gibbs sampler · highest posterior density intervals,Kaplan-Meier estimator,Bayesian setup,log-normal distribution,P-P plot,generalized version | Journal | 27 |
Issue | ISSN | Citations |
1 | 1613-9658 | 1 |
PageRank | References | Authors |
0.48 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bhupendra Singh | 1 | 35 | 5.44 |
| Ajay K. Sharma | 2 | 139 | 25.90 |
| Shubhi Rathi | 3 | 1 | 0.48 |
| Gajraj Singh | 4 | 1 | 0.48 |