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Despite of the great efforts during the censuses, occurrence of somenonsampling errors such as coverage error is inevitable. Coverage error whichcan be classified into two types of under-count and overcount occurs when thereis no unique bijective (one-to-one) mapping between the individuals from thecensus count and the target population -- individuals who usually reside in thecountry (de jure residences). There are variety of reasons make the coverageerror happens including deficiencies in the census maps, errors in the fieldoperations or disinclination of people for participation in the undercountsituation and multiple enumeration of individuals or those who do not belong tothe scope of the census in the overcount situation. A routine practice forestimating the net coverage error is subtracting the census count from theestimated true population, which obtained from a dual system (orcapture-recapture) technique. Estimated coverage error usually suffers fromsignificant uncertainty of the direct estimate of true population or othererrors such as matching error. To rectify the above-mentioned problem andpredict a more reliable coverage error rate, we propose a set ofspatio-temporal mixed models. In an illustrative study on the 2010 censuscoverage error rate of the U.S. counties with population more than 100,000, weselect the best mixed model for prediction by deviance information criteria(DIC) and conditional predictive ordinate (CPO). Our proposed approach forpredicting coverage error rate and its measure of uncertainty is a fullBayesian approach, which leads to a reasonable improvement over the directcoverage error rate in terms of mean squared error (MSE) and confidenceinterval (CI) as provided by the U.S. Census Bureau.

Spatio-Temporal Mixed Models to Predict Coverage Error Rates at Local Areas