Affiliations: [a] Deparment of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia | [b] Department of Statistics, College of Science, Debre Tabor University, Debre Tabor, Ethiopia | [c] Department of Statistics, University of Johannesburg, Johannesburg, South Africa | [d] Department of Statistics, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa | [e] School of Public Health, Bahir Dar University, Bahir Dar, Ethiopia
Correspondence:
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Corresponding author: Seyifemickael Amare Yilema, Deparment of Statistics, College of Science, Bahir Dar University, PO Box 79, Bahir Dar, Ethiopia. Tel.: +251 910 834 038; E-mail: [email protected].
Abstract: A survey is typically designed to produce reliable estimates of target variables of the population at national and regional levels. For unplanned zones with small sample sizes, reliable estimates are needed in many ways but the direct survey estimates are unreliable. The purpose of the study is to improve the direct survey estimates of the z scores of malnutrition for unplanned zones by borrowing auxiliary variables from the census. We applied small area estimations under Fay Herriot (FH) model to overcome the problem of generating reliable estimates by linking the Ethiopian demographic and health survey (DHS) with the census data. According to the results of diagnostic measures, the FH model assumptions are satisfactorily confirmed. And also the results of model-based estimates confirmed that the EBLUPs of z scores of malnutrition are produced more reliable, efficient and precise estimates than the direct survey estimates for small sample sizes in all zones. Therefore, direct survey estimates of malnutrition were highly improved by the EBLUPs in all zones. Zones are important domains for planning and monitoring purposes in the country and therefore z scores of malnutrition estimates for under-five children at the zonal level can be helpful for resource allocation, policymakers, and planners.
Keywords: Fay-Herriot model, survey, census, coefficient of variation, precision, malnutrition
