This paper will discuss the estimation results of Weibull distribution with type 1 right-censored data using numerical methods. These methods involve simulations employing the Maximum Likelihood Estimation technique, utilizing both the Quasi-Newtonrule and the Nelder-Mead simplex algorithm. The simulation includes generating random sample data from distribution with sample nsizes of 500 and 1000. The parameters used for the initial guess are obtained from example data of patients with lung cancer, specifically 2, 3k. Based on the simulation results of the two estimation methods, it is evident that parameter estimation using the Quasi-Newtonrule outperforms the Nelder-Mead simplex algorithm when in an uncensored state.However, the estimated results of the Nelder-Mead method show better estimated values compared to the Quasi-Newtonrule after a fixed censoring time.[see, graphs and tables below]
This paper will discuss the estimation results of Weibull distribution with type 1 right-censored data using numerical methods. These methods involve simulations employing the Maximum Likelihood Estimation technique, utilizing both the Quasi-Newtonrule and the Nelder-Mead simplex algorithm. The simulation includes generating random sample data from distribution with sample nsizes of 500 and 1000. The parameters used for the initial guess are obtained from example data of patients with lung cancer, specifically 2, 3k. Based on the simulation results of the two estimation methods, it is evident that parameter estimation using the Quasi-Newtonrule outperforms the Nelder-Mead simplex algorithm when in an uncensored state.However, the estimated results of the Nelder-Mead method show better estimated values compared to the Quasi-Newtonrule after a fixed censoring time.[see, graphs and tables below]
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Berdimuradov .. | PhD | National University of Uzbekistan |
| № | Havola nomi |
|---|---|
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