Modeling Lifetime Data with Application to Fatigue Models
基于物理和统计考量，提出一个含协变量的五参数寿命数据新模型，给出两种参数与分位数估计方法，并通过模拟和实例验证了回归估计器与顺序统计量估计器的不同优势。
Abstract A new model for the analysis of lifetime data in the presence of a covariate is derived based on physical and statistical considerations. The model depends on five parameters that have clear physical interpretations. Two methods for the estimation of the parameters and of the quantiles are presented: one based on the order statistics and the other a regression estimator. The quantile estimators as well as the estimators of four of the five parameters are given in closed form. The fifth parameter can be estimated independently of the other parameters using either a closed form or a numerically simple algorithm. A simulation study shows that the regression estimators are better for estimating the parameters but the order statistics estimators perform better for the estimation of low quantiles. The methodology is also illustrated by an example of a real life data. Key Words: BootstrapEstimationGeneralize reversed Pareto distributionOrder statisticsQuantile estimationRegression estimator