The Existence of Asymptotically Unbiased Nonnegative Quadratic Estimates of Variance Components in ANOVA Models
研究了在一般方差分析模型中，单个方差分量存在渐近无偏非负二次估计的条件，给出了简单必要条件，并在随机平衡嵌套分类模型和随机不平衡单因素模型中得到充要条件，提醒实际应用时需谨慎。
Abstract Conditions for the existence of asymptotically unbiased, nonnegative quadratic estimates of individual variance components in general ANOVA models are investigated. A simple necessary condition for general models is obtained. Necessary and sufficient conditions are obtained in the case of random, balanced nested classification models and the random unbalanced one-way model. The investigation demonstrates the need for exercising caution when employing nonnegative quadratic estimates of individual variance components in practice. Key Words: Balanced nested classification modelUnbalanced one-way modelAsymptotic unbiasedness