在无反应的情况下估计群体平均值的推断，并应用于曼谷的细颗粒密度

IF 1.4 3区社会学 Q3 DEMOGRAPHY

Mathematical Population Studies Pub Date : 2022-01-19 DOI:10.1080/08898480.2021.1997466

Kanisa Chodjuntug, Nuanpan Lawson

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引用次数: 3

摘要

摘要泰国曼谷的空气污染主要是由废气中排放的细颗粒物造成的。然而，许多关于细颗粒浓度的数据缺失，这一事实可能会使统计数据产生偏差。最小化均方误差的指数型插补允许估计这些浓度的缺失值，并提供平均浓度水平的均方误差较小的估计值。计算了该估计器的偏差和均方误差。模拟表明，在50次观测中，相对效率提高了5%，在100次观测中提高了12%，在200次观测中增加了25%。应用于曼谷细颗粒物浓度的测量，平均水平为47.40微克/立方米，均方误差为0.73微克/立方米平方，而选择的最佳替代估算器的均方误差是0.90微克/立方米立方，平均水平是48.20微克/立方米。

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Imputation for estimating the population mean in the presence of nonresponse, with application to fine particle density in Bangkok

ABSTRACT Air pollution in Bangkok, Thailand, is mainly due to fine particles emitted in exhaust gases. However, many data on fine particle concentrations are missing, a fact which may bias the statistics. Exponential-type imputation minimizing the mean square error allows for estimating the missing values of these concentrations and provides an estimate with smaller mean square error of the mean concentration levels. The bias and mean square error of the proposed estimator are calculated. Simulation shows that the relative efficiency is 5% higher up to 50 observations, 12% higher for 100 observations, and 25% higher for 200 observations. Application to the measurement of fine particle concentration in Bangkok yields a mean square error of 0.73 micrograms per cubic meter squared, for a mean level of 47.40 micrograms per cubic meter, while the mean square error by the best alternative estimator selected is 0.90 micrograms per cubic meter squared, for a mean level of 48.20 micrograms per cubic meter.

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来源期刊

Mathematical Population Studies 数学-数学跨学科应用

CiteScore

3.20

自引率

11.10%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical Population Studies publishes carefully selected research papers in the mathematical and statistical study of populations. The journal is strongly interdisciplinary and invites contributions by mathematicians, demographers, (bio)statisticians, sociologists, economists, biologists, epidemiologists, actuaries, geographers, and others who are interested in the mathematical formulation of population-related questions. The scope covers both theoretical and empirical work. Manuscripts should be sent to Manuscript central for review. The editor-in-chief has final say on the suitability for publication.