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Using some methods for estimating the survival times of patients infected with Covid-19 utilizing new two parameters Lindley distribution NTPLD | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 13، شماره 1، خرداد 2022، صفحه 3213-326 اصل مقاله (587.67 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.6073 | ||
| نویسندگان | ||
| Zahraa Khaled Gaafar* ؛ Mohammed Habeeb AL-Sharoot | ||
| Al Qadisiyah University, Al Diwaniyah, Al-Qādisiyyah, Iraq | ||
| تاریخ دریافت: 11 اسفند 1399، تاریخ بازنگری: 19 خرداد 1400، تاریخ پذیرش: 27 مرداد 1400 | ||
| چکیده | ||
| The aim of this paper is the needs to analyze the survival time for patients with Covide-19b who remains in the hospital until death, so it is necessary to study the survival times and estimate the reliability. The problem of finding the best distribution that fits the data is the key idea to analyze the data accurately. Consequently, the misspecifying of the distribution that fit the data leads to poor quality inference criteria of the phenomenon, also leads to unreliable reliability estimations. Many data of sciences areas are of different probability distributions depending on the nature of the phenomenon within the studied communities Some of the data are represented simple phenomena that cope with a unique probability distribution, and some of which are very complex and heterogeneous systems that force the researchers to use probability distributions fits the behavior of this random phenomenon. Many works in the field of reliability, failure and survival times, and the function of reliability (survival) follows some common distributions such as the Exponential distribution, Weibull distribution and other distributions. In this paper we introduced the survival function that follows an important distribution in survival modeling, that is called the Lindley distribution with two Parameters, taking into account two forms of this distribution, one of them we proposed based on different forms of the probability density function and finding the survival function for the distribution and compared to other distributions using several methods of estimation including the Maximum Likelihood Estimator (MLE), (percentiles estimators) by using Monte Carlo simulation experiments and comparing using the Integrated Mean Square Error (IMSE), (-2$ \ln L $) and AIC to achieve the best estimate of survival function among the distributions, as well as a real data analysis conducted for the survival times for patients with COVID-19 stay in hospital until death. The proposed distribution fitted the data very well in the Maximum Likelihood method compared with the other distribution. | ||
| کلیدواژه‌ها | ||
| Maximum Likelihood؛ Percentiles؛ Lindley Distribution؛ Survival function | ||
| مراجع | ||
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[1] A. Loganathan and M. Uma, Comparison of estimation methods for inverse weibull parameters, Global Stoch. Anal. 4 (1) (2017) 83–84. [2] C. Li and Y. Liu, Asymptotic properties of the maximum likelihood estimator in endogenous regime-switching models, arXiv preprint arXiv:2010.04930, 2020. [3] M. Nagy and M.M. Mohie El-Din, Estimation for inverse weibull distribution under generalized progressive hybrid censoring scheme, J. Stat. Appl. Pro. Lett. 4(3) (2017) 97–107. [4] R. Shanker and S. Sharma, A two-parameter Lindley distribution for Modeling Waiting and Survival Times Data, Appl. Math. 4(2) (2013) 363–368. [5] R.F. Stapelberg, Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design, Springer Science & Business Media, 2009. [6] A. S. Yadav, S.K. Singh and U. Singh, Statistical properties and different methods of estimation for extended weighted inverted Rayleigh distribution, Statist. Transition New Ser. 21 (2) (2020) 119–141. | ||
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