Small area estimation for semicontinuous skewed spatial data: An application to the grape wine production in Tuscany. Issue 1 (11th November 2013)

• Record Type: Journal Article
• Authors: Dreassi, Emanuela; Petrucci, Alessandra; Rocco, Emilia
• Is Part Of: Biometrical journal. Volume 56:Issue 1(2014:Jan.)
• Page Start: 141
• Page End: 156
• Publication Date: 2013-11-11
• Subjects: Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201200271
• Languages: English
• ISSNs: 0323-3847

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Linear‐mixed models are frequently used to obtain model‐based estimators in small area estimation (SAE) problems. Such models, however, are not suitable when the target variable exhibits a point mass at zero, a highly skewed distribution of the nonzero values and a strong spatial structure. In this paper, a SAE approach for dealing with such variables is suggested. We propose a two‐part random effects SAE model that includes a correlation structure on the area random effects that appears in the two parts and incorporates a bivariate smooth function of the geographical coordinates of units. To account for the skewness of the distribution of the positive values of the response variable, a Gamma model is adopted. To fit the model, to get small area estimates and to evaluate their precision, a hierarchical Bayesian approach is used. The study is motivated by a real SAE problem. We focus on estimation of the per‐farm average grape wine production in Tuscany, at subregional level, using the Farm Structure Survey data. Results from this real data application and those obtained by a model‐based simulation experiment show a satisfactory performance of the suggested SAE approach.</p> </abstract>