Mass imputation for two-phase sampling

被引：3
|
作者：
Park, Seho [1 ]
Kim, Jae Kwang [1 ]
机构：
[1] Iowa State Univ, Dept Stat, Ames, IA 50011 USA
来源：
JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2019年 / 48卷 / 04期
基金：
美国国家科学基金会;
关键词：
Auxiliary information; Categorical data; Domain estimation; Outcome-dependent sampling; VARIANCE-ESTIMATION; MISSING DATA;
D O I：
10.1016/j.jkss.2019.03.002
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：
Two-phase sampling is a cost-effective method of data collection using outcome-dependent sampling for the second-phase sample. In order to make efficient use of auxiliary information and to improve domain estimation, mass imputation can be used in two-phase sampling. Rao and Sitter (1995) introduce mass imputation for two-phase sampling and its variance estimation under simple random sampling in both phases. In this paper, we extend the Rao-Sitter method to general sampling design. The proposed method is further extended to mass imputation for categorical data. A limited simulation study is performed to examine the performance of the proposed methods. (C) 2019 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：578 / 592
页数：15
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