一类不完全数据下指数分布的参数估计
Parameter Estimation of Exponential Distribution under a Class of Incomplete Data

本文针对指数分布，依据极大似然估计方法和贝叶斯估计方法，研究了在右删失数据下指数分布密度函数中参数λ的估计问题，并通过实验数据比较了两种方法的估计效果。研究表明在样本容量相同的情况下，贝叶斯估计方法要比极大似然估计方法偏差更小，可信度更高。
This paper studies the estimation problem of parameter λ in the exponential distribution density function under right censored data based on the maximum likelihood estimation method and Bayes estimation method and compares the estimation efficiency of the two methods through experimental data. The results show that under the same sample size, the Bayes estimation method has a smaller deviation and higher reliability than the maximum likelihood estimation method.