The linear least-squares method has been applied to Weibull distribution for analysing the reliability, and the exact confidence intervals for Weibull parameters can be constructed from both Type-I and Type-II censored data. However, this method changes the shape of theoretical linear fit and estimates are highly biased for heavily censored data. Therefore, the nonlinear method (NLLSM) and transformation-based least-squares methods (TBLSM) are proposed in the literature. In this paper, I address confidence intervals for Weibull parameters based on the two methods and discuss the reliability and remaining lifetime with the right censored data. I propose the exact confidence intervals from pivotal quantities for the Weibull parameters based on NSLLM and approximate ones based on TBLLM. Further, different methods are compared through a Monte Carlo simulation study. Finally, these methods are applied to a data set as an illustrative example.

线性最小二乘法已应用于Weibull分布以分析可靠性，并且Weibull参数的确切置信区间可以从I型和II型检查数据中构造。但是，这种方法改变了理论线性拟合的形状，并且对于严格审​​查的数据，估计值存在很大偏差。因此，文献中提出了非线性方法（NLLSM）和基于变换的最小二乘法（TBLSM）。在本文中，我将基于这两种方法处理威布尔参数的置信区间，并讨论使用正确的删失数据的可靠性和剩余寿命。我提出了基于NSLLM的Weibull参数的关键量的确切置信区间，以及基于TBLLM的近似参数的置信区间。进一步，通过蒙特卡洛模拟研究比较了不同的方法。最后，将这些方法应用于数据集作为说明性示例。