The implementation of maximum likelihood estimation for the model parameters is discussed and, nally, the model is fitted to a real database (Melanoma data set), and comparisons are performed with alternatives to the new $S_0(\cdot)$ .
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper we consider the geometric cure rate model defined in [16], using for $S_0(\cdot)$, the survival function of carcinogenic cells, an extension of the half-normal distribution based on the distribution of the maximum of a random sample.
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Key Findings & Data:In this paper we consider the geometric cure rate model defined in [16], using for $S_0(\cdot)$, the survival function of carcinogenic cells, an extension of the half-normal distribution based on the distribution of the maximum of a random sample.
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Conclusion & Impact:The implementation of maximum likelihood estimation for the model parameters is discussed and, nally, the model is fitted to a real database (Melanoma data set), and comparisons are performed with alternatives to the new $S_0(\cdot)$ .
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Cure rate survival model · Half-normal distribution · Maximum likelihood
Abstract
In this paper we consider the geometric cure rate model defined in [16], using for $S_0(\cdot)$, the survival function of carcinogenic cells, an extension of the half-normal distribution based on the distribution of the maximum of a random sample. The implementation of maximum likelihood estimation for the model parameters is discussed and, nally, the model is fitted to a real database (Melanoma data set), and comparisons are performed with alternatives to the new $S_0(\cdot)$ .
Gómez, Y. M., Bolfarine, H. (2018). The geometric power half-normal regression model with cure rate. Hacettepe Journal of Mathematics and Statistics, Volume: 47 Issue: 2. https://ajindex.com/articles/1402959
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