Empirical Bayes small area prediction under a zero-inflated lognormal model with correlated random area effects.

correlated random effects empirical Bayes sheet and rill erosion small area prediction zero-inflated lognormal

Journal

Biometrical journal. Biometrische Zeitschrift

ISSN: 1521-4036

Titre abrégé: Biom J

Pays: Germany

ID NLM: 7708048

Informations de publication

Date de publication:
Dec 2020

Historique:

revised: 05 07 2020

received: 23 01 2020

accepted: 06 07 2020

medline: 30 7 2020

pubmed: 30 7 2020

entrez: 30 7 2020

Statut: ppublish

Résumé

Many variables of interest in agricultural or economical surveys have skewed distributions and can equal zero. Our data are measures of sheet and rill erosion called Revised Universal Soil Loss Equation - 2 (RUSLE2). Small area estimates of mean RUSLE2 erosion are of interest. We use a zero-inflated lognormal mixed effects model for small area estimation. The model combines a unit-level lognormal model for the positive RUSLE2 responses with a unit-level logistic mixed effects model for the binary indicator that the response is nonzero. In the Conservation Effects Assessment Project (CEAP) data, counties with a higher probability of nonzero responses also tend to have a higher mean among the positive RUSLE2 values. We capture this property of the data through an assumption that the pair of random effects for a county are correlated. We develop empirical Bayes (EB) small area predictors and a bootstrap estimator of the mean squared error (MSE). In simulations, the proposed predictor is superior to simpler alternatives. We then apply the method to construct EB predictors of mean RUSLE2 erosion for South Dakota counties. To obtain auxiliary variables for the population of cropland in South Dakota, we integrate a satellite-derived land cover map with a geographic database of soil properties. We provide an R Shiny application called viscover (available at https://lyux.shinyapps.io/viscover/) to visualize the overlay operations required to construct the covariates. On the basis of bootstrap estimates of the mean square error, we conclude that the EB predictors of mean RUSLE2 erosion are superior to direct estimators.

Identifiants

DOI: 10.1002/bimj.202000029 PMID: 32725804

pubmed: 32725804

doi: 10.1002/bimj.202000029

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

1859-1878

Subventions

Organisme : Natural Resources Conservation Service

ID : 017301-00001

Organisme : Division of Social and Economic Sciences

ID : 1733572

Informations de copyright

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