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拟似然及其应用：最优参数估计的一般方法AI 解读

The General Framework.- An Alternative Approach: E-Sufficiency.- Asymptotic Confidence Zones of Minimum Size.- Asymptotic Quasi-Likelihood.- Combining Estimating Functions.- Projected Quasi-Likelihood.- Bypassing the Likelihood.- Hypothesis Testing.- Infinite Dimensional Problems.- Miscellaneous Applications.- Consistency and Asymptotic Normality for Estimating Functions.- Complements and Strategies for Application.

摘要暂缺

鞅差误差回归模型中最小二乘估计强一致性的一则说明AI 解读

Conditions for the pointwise consistency of weighted least squares estimators from multivariate regression models with martingale difference errors are given in terms of the relative rates at which certain quadratic forms diverge to infinity.

随机过程的条件最小二乘估计AI 解读

An estimation procedure for stochastic processes based on the minimization of a sum of squared deviations about conditional expectations is developed. Strong consistency, asymptotic joint normality and an iterated logarithm rate of convergence are shown to hold for the estimators under a variety of conditions. Special attention is given to the widely studied cases of stationary ergodic processes and Markov processes with are asymptotically stationary and ergodic. The…

位置尺度族中次序统计量的最优与简化线性不变预测AI 解读

We observe the early failures of identical items simultaneously put under stress and use their failure times to predict later failure times in the same sample. Best linear invariant predictors and two simplified predictors are presented and their relative behaviour for some specific distributions is indicated in a table.

位置尺度族中次序统计量的最佳线性无偏预测AI 解读

In many life testing, reliability and replacement policy situations, it is desirable to predict the time of future failures from times of the early failures in the same sample. Interval prediction in this setting has been treated by several authors. In this article, the authors give best linear unbiased (blu) point predictors of future failures when the underlying distribution is known up to location and scale. Analogous large sample procedures based on sample quantile…