In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data. Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated. The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters. The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme. The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model. The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.
This paper describes the development of a prediction model for the early identification of low employability graduates in Malaysia. A total of five proportional hazard models are estimated and using the criteria of percentage correctly and wrongly predicted, a prediction model is selected based on the percentage correctly predicted. The percentile of the predicted hazard rate is used as the employability index (EI). In the context of Malaysia, it is recommended that the 5th percentile graduates be considered as low employability graduates. With this early identification tool, specific intervention programs can be tailored for the right target groups.
Aims: In this study a survival mixture model of three components is considered to analyse survival data of heterogeneous nature. The survival mixture model is of the Exponential, Gamma and Weibull distributions. Methodology: The proposed model was investigated and the Maximum Likelihood (ML) estimators of the parameters of the model were evaluated by the application of the Expectation Maximization Algorithm (EM). Graphs, log likelihood (LL) and the Akaike Information Criterion (AIC) were used to compare the proposed model with the pure classical parametric survival models corresponding to each component using real survival data. The model was compared with the survival mixture models corresponding to each component. Results: The graphs, LL and AIC values showed that the proposed model fits the real data better than the pure classical survival models corresponding to each component. Also the proposed model fits the real data better than the survival mixture models corresponding to each component. Conclusion: The proposed model showed that survival mixture models are flexible and maintain the features of the pure classical survival model and are better option for modelling heterogeneous survival data.
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