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基于 II 型删失数据的极大似然及相关估计量的渐近性质AI 解读

Some simple procedures are provided for establishing the asymptotic normality and uniform strong convergence of a class of functions that arise in the context of estimating parameters from a Type II censored sample. These lead to an elementary yet rigorous treatment of the asymptotic properties of maximum likelihood estimators based on Type II censored data. Further applications include the treatment of asymptotics of some modified maximum likelihood (MML) estimators. In…

逆高斯模型下双因素实验的分析AI 解读

This article treats the analysis of factorial experiments under an inverse Gaussian distribution for the failure times. A reciprocal-linear model for the factor effects is motivated from the context of the underlying Wiener process. Explicit solutions to the likelihood equations are derived, and important properties such as strong consistency and limiting normality are established. A least squares approach using the reciprocals of the sample cell means is also studied and…

逆高斯模型下的双因素实验分析AI 解读

This article treats the analysis of factorial experiments under an inverse Gaussian distribution for the failure times. A reciprocal-linear model for the factor effects is motivated from the context of the underlying Wiener process. Explicit solutions to the likelihood equations are derived, and important properties such as strong consistency and limiting normality are established. A least squares approach using the reciprocals of the sample cell means is also studied and…

第二类删失下局部最强秩检验的统一处理AI 解读

For the derivation of locally most powerful (LMP) rank tests, a general method that encompasses a wide variety of testing problems and type II censoring schemes is formulated. Applications are illustrated in the context of the one-sample, two-sample, and regression problems. It is shown that the traditional derivations of the LMP rank tests are greatly simplified with the proposed method. The LMP criterion is also applied to some problems for which only ad hoc tests were…

两个指数分布联合Ⅱ型删失样本的置信区间AI 解读

Strong consistency and asymptotic normality of the maximum likelihood estimators are established in the context of jointly type-II censored samples from two exponential populations. Large-sample confidence intervals are derived for the individual scale parameters as well as their ratio, and some applications to series and parallel systems are discussed. For the ratio of the scale parameters, an exact confidence procedure is developed on the basis of the failure counts in the…

两个指数分布联合Ⅱ型删失样本的置信区间AI 解读

Strong consistency and asymptotic normality of the maximum likelihood estimators are established in the context of jointly type-II censored samples from two exponential populations. Large-sample confidence intervals are derived for the individual scale parameters as well as their ratio, and some applications to series and parallel systems are discussed. For the ratio of the scale parameters, an exact confidence procedure is developed on the basis of the failure counts in the…

联合第二类删失下两个指数分布的相等性检验AI 解读

The problem of testing equality of the scale parameters of two exponential distributions is considered under a combined sample type II censoring scheme that has been extensively treated in nonparametric inference. Sufficiency and invariance considerations lead to a pair of statistics, namely, the proportions of failure count and the total time on test of the first sample to those of the combined sample. Exact distribution and moment properties are discussed, and asymptotic…