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Exploring recursion for optimal estimators under cascade rotation

机译:探索级联旋转下最优估计的递归

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We are concerned with optimal linear estimation of means on subsequent occasions under sample rotation where evolution of samples in time is designed through a cascade pattern. It has been known since the seminal paper of Patterson (1950) that when the units are not allowed to return to the sample after leaving it for certain period (there are no gaps in the rotation pattern), one step recursion for optimal estimator holds. However, in some important real surveys, e.g., Current Population Survey in the US or Labour Force Survey in many countries in Europe, units return to the sample after being absent in the sample for several occasions (there are gaps in rotation patterns). In such situations difficulty of the question of the form of the recurrence for optimal estimator increases drastically. This issue has not been resolved yet. Instead alternative sub-optimal approaches were developed, as K - composite estimation (see e.g., Hansen, Hurwitz, Nisselson and Steinberg (1955)), AK - composite estimation (see e.g., Gurney and Daly (1965)) or time series approach (see e.g., Binder and Hidiroglou (1988)).
机译:我们关注样本旋转下随后情况下的均值的最佳线性估计,其中样本的时间演化是通过级联模式设计的。自Patterson(1950)的开创性论文以来,就已经知道,当样品离开一定时间后(在旋转模式中没有间隙),不允许单位返回样品时,可以进行一步递归以获得最优估计量。但是,在一些重要的实际调查中,例如,美国的当前人口调查或欧洲的许多国家的劳动力调查,单位在多次缺席后会返回样本(轮换方式存在差距)。在这种情况下,最优估计器的递归形式问题的难度急剧增加。此问题尚未解决。取而代之的是开发替代的次优方法,例如K-综合估计(参见例如Hansen,Hurwitz,Nisselson和Steinberg(1955)),AK-综合估计(参见例如Gurney和Daly(1965))或时间序列方法(参见例如Binder和Hidiroglou(1988)。

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