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A comprehensive assessment of the performance and stability of maximum likelihood estimators for the exponential–poisson distribution: An analysis of sample size and censoring schemes on the NR, EM, and SEM algorithms under type I progressive interval censoring | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 16 اردیبهشت 1405 اصل مقاله (461.04 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2026.39931.5588 | ||
| نویسندگان | ||
| Ahmad Mohammadi؛ Ali Shadrokh* ؛ Masoud Yarmohammadi | ||
| Department of Statistics, Payame Noor University (PNU), P.O.Box 19395-4697, Tehran, Iran | ||
| تاریخ دریافت: 12 آذر 1404، تاریخ بازنگری: 13 بهمن 1404، تاریخ پذیرش: 13 بهمن 1404 | ||
| چکیده | ||
| This study compares Newton-Raphson (NR), EM, and stochastic EM (SEM) for estimating Exponential-Poisson parameters under Type I progressive interval censoring. Simulations across sample sizes (20–200) and censoring schemes show SEM achieves the smallest MSE and highest stability, especially for small samples or complex censoring. EM performs reliably for large samples but is more variable for small samples. NR is highly sensitive to initial values and least stable. A real melanoma dataset confirms EP distribution fit (KS=0.0961) and replicates simulation patterns, emphasizing the need to choose estimation methods carefully. | ||
| کلیدواژهها | ||
| exponential-poisson distribution؛ type I progressive interval censoring؛ maximum likelihood estimator؛ SEM algorithm؛ EM algorithm؛ NR algorithm؛ mean squared error (MSE) | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 3 تعداد دریافت فایل اصل مقاله: 4 |
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