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A nested error regression model with high-dimensional parameter for small area estimation
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OA
AI
DOI：10.1093/jrsssb/qkac010.png)
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In this paper, we propose a flexible nested error regression small area model with high-dimensional parameter that incorporates heterogeneity in regression coefficients and variance components. We develop a new robust small area-specific estimating equations method that allows appropriate pooling of a large number of areas in estimating small area-specific model parameters. We propose a parametric bootstrap and jackknife method to estimate not only the mean squared errors but also other commonly used uncertainty measures such as standard errors and coefficients of variation. We conduct both model-based and design-based simulation experiments and real-life data analysis to evaluate the proposed methodology.
Keyword:
Australian Agricultural Grazing Industry Survey data
design consistency
Environmental Monitoring and Assessment Program data
M-estimation
root mean squared error estimation
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
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