Small Area Estimation with Random Forests and the LASSO Article Swipe

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Victoire Michal , Jon Wakefield , Alexandra M. Schmidt , Alicia Cavanaugh , Brian E. Robinson , Jill Baumgartner · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2308.15180

We consider random forests and LASSO methods for model-based small area estimation when the number of areas with sampled data is a small fraction of the total areas for which estimates are required. Abundant auxiliary information is available for the sampled areas, from the survey, and for all areas, from an exterior source, and the goal is to use auxiliary variables to predict the outcome of interest. We compare areal-level random forests and LASSO approaches to a frequentist forward variable selection approach and a Bayesian shrinkage method. Further, to measure the uncertainty of estimates obtained from random forests and the LASSO, we propose a modification of the split conformal procedure that relaxes the assumption of identically distributed data. This work is motivated by Ghanaian data available from the sixth Living Standard Survey (GLSS) and the 2010 Population and Housing Census. We estimate the areal mean household log consumption using both datasets. The outcome variable is measured only in the GLSS for 3\% of all the areas (136 out of 5019) and more than 170 potential covariates are available from both datasets. Among the four modelling methods considered, the Bayesian shrinkage performed the best in terms of bias, MSE and prediction interval coverages and scores, as assessed through a cross-validation study. We find substantial between-area variation, the log consumption areal point estimates showing a 1.3-fold variation across the GAMA region. The western areas are the poorest while the Accra Metropolitan Area district gathers the richest areas.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2308.15180

PDF

https://arxiv.org/pdf/2308.15180

OA Status

green

Related Works

10

OpenAlex ID

https://openalex.org/W4386302626

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OpenAlex ID

https://openalex.org/W4386302626

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DOI

https://doi.org/10.48550/arxiv.2308.15180

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Title

Small Area Estimation with Random Forests and the LASSO

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Type

preprint

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Language

en

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Publication year

2023

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Publication date

2023-08-29

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Authors

Victoire Michal, Jon Wakefield, Alexandra M. Schmidt, Alicia Cavanaugh, Brian E. Robinson, Jill Baumgartner

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Landing page

https://arxiv.org/abs/2308.15180

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PDF URL

https://arxiv.org/pdf/2308.15180

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2308.15180

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Concepts

Small area estimation, Covariate, Lasso (programming language), Statistics, Random forest, Frequentist inference, Random effects model, Point estimation, Independent and identically distributed random variables, Bayesian probability, Geography, Population, Econometrics, Estimator, Mathematics, Bayesian inference, Random variable, Computer science, Machine learning, Meta-analysis, Internal medicine, World Wide Web, Sociology, Demography, Medicine

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0

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Related works (count)

10

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