The paper addresses the problem of estimation of the model parameters of the logistic exponential distribution based on progressive type-I hybrid censored sample. The maximum likelihood estimates are obtained and computed numerically using Newton-Raphson method. Further, the Bayes estimates are derived under squared error, LINEX and generalized entropy loss functions. Two types (independent and bivariate) of prior distributions are considered for the purpose of Bayesian estimation. It is seen that the Bayes estimates are not of explicit forms.Thus, Lindley's approximation technique is employed to get approximate Bayes estimates. Interval estimates of the parameters based on normal approximate of the maximum likelihood estimates and normal approximation of the log-transformed maximum likelihood estimates are constructed. The highest posterior density credible intervals are obtained by using the importance sampling method. Furthermore, numerical computations are reported to review some of the results obtained in the paper. A real life dataset is considered for the purpose of illustrations.

翻译：本文研究了基于渐进式I型混合删失样本的逻辑斯蒂指数分布模型参数估计问题。通过Newton-Raphson方法数值计算得到了最大似然估计。进一步，在平方误差、LINEX和广义熵损失函数下推导了贝叶斯估计。贝叶斯估计中考虑了两种类型的先验分布（独立和二元）。鉴于贝叶斯估计不具备显式表达式，采用Lindley近似方法获得近似贝叶斯估计。基于最大似然估计的正态近似和对数变换最大似然估计的正态近似，构建了参数的区间估计。通过重要性抽样方法获得最高后验密度可信区间。此外，通过数值计算验证了文中部分结果，并基于真实数据集进行了实例说明。