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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Comparison of Survival Studies for Some Parametric Distributions Models for the Breast Cancer Data Using Maximum Likelihood Method

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ISSN 2079-8407
On Pages 1159-1165
Volume No. 3
Issue No. 8
Issue Date August 01, 2012
Publishing Date August 01, 2012
Keywords Inverse Gaussian model, Gumbel and Weibull models, Censoring, Breast Cancer Data sets, BFGS-unconstrained optimization method, Maximum likelihood function and Kaplan-Meier survivor rate estimates


The survival rate estimates for the breast cancer censored data have been considered for the 254 patients. The data [11] was treated at the chemotherapy department, Bradford Royal Infirmary for ten years. Here in this paper Inverse Gaussian, Gumbel and Weibull probability distribution (see [13], [14], [15]) models are used to obtain the survival rates of the patients using BFGS (Broyden-Fletcher-Goldfarb-Shanno) variable metric optimization methods. Maximum likelihood method [18] has been used through (BFGS) unconstrained optimization method [3, 6, 7] to find the parameter estimates and variance-covariance matrix for the said distribution models. Finally the survivor rate estimates for the probability models have been compared with the non-parametric Kaplan-Meier [12] method.  


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