A new robust Bayesian small area estimation via ‐stable model for estimating the proportion of athletic students in California

Zarei, Shaho; Arima, Susumu; Lasinio, Giovanna Jona

doi:10.1002/bimj.202000235

Cited by 3 publications

References 33 publications

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Bayesian Small Area Estimation

Polettini¹,

Arima²

2022

Wiley StatsRef: Statistics Reference Online

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The wealth of timely and detailed information provided by sample surveys ( see Survey Sampling; Finite Populations, Sampling From ) attracts interest of researchers and analysts, with planned domains ( see Sampling Designs in Surveys) being of primary yet clearly not exclusive inferential interest. Indeed, based on the sample data, a whole variety of subpopulations could be investigated to comprehend local needs and issues, highlight the geography of socioeconomic disparities, drive the planning and evaluation of policies, and so on. We denote by small area any geographical or demographic domain that is not planned for the survey. Counties, municipalities or states, or the groups obtained by cross‐classification of age, sex, ethnicity, and region are examples. In general, we call direct an estimator solely based on the subsample of units belonging to the area of interest. Accurate design‐based inference may be jeopardized by the small (or even zero) sample size of such domains, whence the terminology. Inferential interest concerns area‐specific parameters, usually means, proportions, or percentiles. Typical applications include estimation of mean household income at the municipality level in poverty studies, the number of persons affected by a given disease in a given sex by age by region classification in epidemiological studies, the crop area under vines at the regional level in agricultural economics, and counts and rates for school age children under poverty at the county and school district levels for allocation of funds. To produce reliable estimates of such aggregates, we use indirect estimators that rely on auxiliary information to connect small areas through explicit or implicit models, thus borrowing strength and increasing the effective sample size. Such process is generally referred to as “small area estimation.” The article Small Area Estimation describes the nature of the problem and the different approaches. This article focuses on Bayesian models for small area estimation. We present two basic linear models and briefly discuss a number of different extensions that are easily accommodated under a Bayesian framework.

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Bayesian Small Area Estimation

Polettini¹,

Arima²

2022

Wiley StatsRef: Statistics Reference Online

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Robust Bayesian small area estimation using the sub-Gaussian $$\alpha$$-stable distribution for measurement error in covariates

Arima,

Zarei

2024

AStA Adv Stat Anal

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Evaluation of physical fitness and health of young children aged between 3 and 6 based on cluster and factor analyses

Zhao,

Li,

Wang

et al. 2024

BMC Public Health

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Background As life improves and sedentary time increases, young children's physical fitness gradually declines. Methods Multi-stage stratified whole cluster sampling was utilized to sample 5584 preschoolers. Young infants' morphology, function, and quality were revealed using cluster and factor analysis. Results The cluster analysis separated 3–6-year-olds into two genders: 1,551 men in group A "high physical fitness" 1,499 men in group B "low physical fitness"; 1,213 women in group A and 1,321 women in group B. Young children's fitness was measured by standing long jump(1.00), weight(1.00), and height(1.00). A cluster analysis of 3–4-year-olds classified them into three groups: 272 “muscular strength,” 75 “average physical fitness,” and 250 “low agility.” Young children's health depends on weight (1.00), height (0.57), and chest circumference (0.54). A cluster analysis of the 4–5-year-olds classified them into two groups: 1070 “balance” and 806 “muscular strength.” Young children’s health depends on weight (1.00), height (0.74), and chest circumference (0.71). A cluster analysis of the 5–6-year-olds divided them into three groups: 1762 “high physical fitness,” 384 “obese,” and 105 “low physical fitness.” Young children’s physical health depends on BMI (1.00), weight (1.00), and chest circumference (1.00). Factor analysis demonstrated that muscle strength, body shape, cardiovascular variables, and physical fitness composite components affected young children's health. Conclusion Women should focus on motor function and strength, while men on flexibility. Male group B “low physical fitness” should focus on strength, motor function, and balance, whereas male group A “high physical fitness” should focus on flexibility. Then, female group A “high physical fitness” should emphasize variety.2) For 3–4-year-olds, group A “muscular strength” should focus on flexibility, and group C “low agility” on motor function. 3) For 4–5-year-olds, group A “balanced” should focus on strength and motor function; 4) For 5–6-year-olds, group B “obese” should emphasize weight loss, and group C “low fitness” should emphasize strength, motor function, and flexibility; 5) Young children’s physical fitness depends on muscle strength, body shape, cardiovascular factors, and physical fitness composite.

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A new robust Bayesian small area estimation via ‐stable model for estimating the proportion of athletic students in California

Cited by 3 publications

References 33 publications

Bayesian Small Area Estimation

Bayesian Small Area Estimation

Robust Bayesian small area estimation using the sub-Gaussian $$\alpha$$-stable distribution for measurement error in covariates

Evaluation of physical fitness and health of young children aged between 3 and 6 based on cluster and factor analyses

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