TY - JOUR ID - 12302 TI - Small Area Estimation and Spaial Prediction JO - Journal of Advanced Mathematical Modeling JA - JAMM LA - en SN - 2251-8088 AU - Mohammadzadeh, Mohsen AU - Naseri, Zahra AD - Professor in Statistics at Tarbiat Modares University AD - Department of Statistics, Tarbiat Modares University Y1 - 2016 PY - 2016 VL - 6 IS - 1 SP - 23 EP - 40 KW - Small Area KW - Linear Mixed Model KW - Spatial Model KW - Spatial Prediction KW - Model Based Estimation DO - 10.22055/jamm.2016.12302 N2 - Direct estimators of parameters are not precise because of little surveys unit in small areas. Regarding the wide spread increase of demand for providing valid and accurate statistics for small areas, attempts have been made to present proper solutions for the problems. Small area estimation approaches provide the direct estimators with borrowing strength to increase their precision based on a model, especially about those estimators that are based on the linear mixed model including random area effects and using various auxiliary sources. Data associated with spatially contiguous small areas may be modeled via covariates, with error terms that are spatially dependent according to neighbor areas. In this paper we investigate small area estimation based on linear models with spatially correlated small area effects where the neighborhood structure is described by a contiguity matrix. Such models allow efficient use of spatial auxiliary information in small area estimation. Then estimation for small areas will be achieved for the amount of agronomy production in Fars province, according to the two common EBLUP and MBDE methods and two usual (non spatial) and spatial approaches based on the unit level model. Then the accuracy of them have been compared. UR - https://jamm.scu.ac.ir/article_12302.html L1 - https://jamm.scu.ac.ir/article_12302_9d8c3926f10c30ca2e737e373a464584.pdf ER -