Morad, N. (2024). Parametric Estimation under Type I Censoring Geometric Distribution with Missing Data. The Egyptian Statistical Journal, 68(2), 1-14. doi: 10.21608/esju.2024.278969.1028
Naglaa Abdelmoneim Morad. "Parametric Estimation under Type I Censoring Geometric Distribution with Missing Data". The Egyptian Statistical Journal, 68, 2, 2024, 1-14. doi: 10.21608/esju.2024.278969.1028
Morad, N. (2024). 'Parametric Estimation under Type I Censoring Geometric Distribution with Missing Data', The Egyptian Statistical Journal, 68(2), pp. 1-14. doi: 10.21608/esju.2024.278969.1028
Morad, N. Parametric Estimation under Type I Censoring Geometric Distribution with Missing Data. The Egyptian Statistical Journal, 2024; 68(2): 1-14. doi: 10.21608/esju.2024.278969.1028
Parametric Estimation under Type I Censoring Geometric Distribution with Missing Data
Department of Applied Statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, 12613, Cairo University, Egypt
Abstract
This manuscript's main objective is to estimate the geometric distribution's parameters using progressive type-I censored data with missing data. The efficiency of estimators is studied. Moreover, the estimators' consistency property is shown. To produce the estimators, the maximum likelihood technique is performed. The geometric distribution is applied to n = 10, 30, and 50 to create samples. The initial values are set as , = 0.2 for one geometric population and = 0.15 for a general parameter involving two geometric populations. = 5,10, and 15 are the censored times. Missing data (δ=η=0) has a percentage of 0.1, 0.2, 0.3, 0.5, and 0.7. The bias criterion and the root mean square error (RMSE) are applied. The outcomes for each of the one and two populations revealed a number of significant markers. Both the bias and the root mean square error increase in percentage to the amount of missing data. Conversely, the bias and root mean square error decreases with increasing sample size and censoring time.
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