This paper describes an application of small area estimation (SAE) to agricultural business survey data. Both well known small area estimators, such as the empirical best linear unbiased predictor (EBLUP), and more recently proposed small area estimators, for example, the M-quantile, the robust EBLUP and the Model Based Direct estimators are considered. Mean squared error estimation is discussed. Using a real agricultural business survey dataset, we place emphasis on model diagnostics for specifying the small area working model, on diagnostic measures for validating the reliability of direct and indirect (model- based) small area estimators and on providing practical guidelines to the prospective user of small area estimation techniques.
Small Area Estimation in Practice: An Application to Agricultural Business Survey Data
SALVATI, NICOLA;
2012-01-01
Abstract
This paper describes an application of small area estimation (SAE) to agricultural business survey data. Both well known small area estimators, such as the empirical best linear unbiased predictor (EBLUP), and more recently proposed small area estimators, for example, the M-quantile, the robust EBLUP and the Model Based Direct estimators are considered. Mean squared error estimation is discussed. Using a real agricultural business survey dataset, we place emphasis on model diagnostics for specifying the small area working model, on diagnostic measures for validating the reliability of direct and indirect (model- based) small area estimators and on providing practical guidelines to the prospective user of small area estimation techniques.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.