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Abstract:
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In this talk, I will consider a competing cause scenario and assume the number of competing causes to follow a Conway-Maxwell Poisson distribution, which can capture both over and under dispersion that is usually encountered in discrete data. Assuming the population of interest having a component cure and the form of the data to be interval censored, as opposed to the usually considered right censored data, I will discuss the steps of the expectation maximization algorithm for the determination of the maximum likelihood estimates of the model parameters. Next, I will present the results of an extensive Monte Carlo simulation study to demonstrate the performance of the proposed estimation method. Model discrimination within the Conway-Maxwell Poisson distribution will be addressed using the likelihood ratio test. Finally, the proposed methodology and the flexibility of the Conway-Maxwell Poisson distribution will be illustrated with a smoking cessation data.
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