The log-logistic distribution has been widely used in several fields, including engineering, survival analysis, and economics. The method of maximum likelihood estimation is used in this study for estimating the shape and scale parameters for the log-logistic distribution, whereas in the case of the log-logistic distribution, likelihood equations lack explicit solutions. Therefore, problems with solving likelihood equations can be solved by using two highly efficient algorithms, which are the whale optimization algorithm and the Nelder-Mead algorithm, as well as by showing the applicability of this distribution by comparing it with other well-known classical distributions. To demonstrate the performance of each algorithm implemented, an extensive Monte Carlo simulation study has been conducted. The performance of maximum likelihood estimators for each algorithm has been evaluated in terms of mean square error and deficiency criteria. It has been seen that the whale optimization algorithm provides the best estimates for the log-logistic distribution parameters according to the simulation data.
Maximum likelihood Log-logistic distribution Whale optimization Monte-Carlo simulation
The log-logistic distribution has been widely used in several fields, including engineering, survival analysis, and economics. The method of maximum likelihood estimation is used in this study for estimating the shape and scale parameters for the log-logistic distribution, whereas in the case of the log-logistic distribution, likelihood equations lack explicit solutions. Therefore, problems with solving likelihood equations can be solved by using two highly efficient algorithms, which are the whale optimization algorithm and the Nelder-Mead algorithm, as well as by showing the applicability of this distribution by comparing it with other well-known classical distributions. To demonstrate the performance of each algorithm implemented, an extensive Monte Carlo simulation study has been conducted. The performance of maximum likelihood estimators for each algorithm has been evaluated in terms of mean square error and deficiency criteria. It has been seen that the whale optimization algorithm provides the best estimates for the log-logistic distribution parameters according to the simulation data.
Maximum likelihood Log-logistic distribution Whale optimization Monte-Carlo simulation
Birincil Dil | İngilizce |
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Konular | Hesaplamalı İstatistik, İstatistiksel Analiz, İstatistiksel Teori, Uygulamalı İstatistik |
Bölüm | Research Articles |
Yazarlar | |
Erken Görünüm Tarihi | 5 Ekim 2023 |
Yayımlanma Tarihi | 15 Ekim 2023 |
Gönderilme Tarihi | 6 Eylül 2023 |
Kabul Tarihi | 4 Ekim 2023 |
Yayımlandığı Sayı | Yıl 2023 |