A finite population quantile estimation by unequal probability sampling

被引：2

作者：

Chaudhuri, Arijit ^{[1
]}

Shaw, Purnima ^{[2
]}

机构：

[1] Indian Stat Inst, Appl Stat Unit, Kolkata, India

[2] Reserve Bank India, Mumbai, Maharashtra, India

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2020年 / 49卷 / 22期

关键词：

Distribution function; model; predictor; quantile; super-population; varying probability sample-selection;

D O I：

10.1080/03610926.2019.1618475

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Using a model-assisted approach, this paper studies asymptotically design-unbiased (ADU) estimation of a population "distribution function" and extends to deriving an asymptotic and approximate unbiased estimator for a population quantile from a sample chosen with varying probabilities. The respective asymptotic standard errors and confidence intervals are then worked out. Numerical findings based on an actual data support the theory with efficient results.

引用

页码：5419 / 5426

页数：8

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