Objective To assess the data quality and estimate the provincial infant mortality rate（1q0） from China＇s sixth census. Methods A log-quadratic model is applied to under-fifteen data. We analyze and compare the aver...Objective To assess the data quality and estimate the provincial infant mortality rate（1q0） from China＇s sixth census. Methods A log-quadratic model is applied to under-fifteen data. We analyze and compare the average relative errors（AREs） for 1q0 between the estimated and reported values using the leave-one-out cross-validation method. Results For the sixth census, the AREs are more than 100% for almost all provinces. The estimated average 1q0 level for 31 provinces is 12.3‰ for males and 10.7‰ for females. Conclusion The data for the provincial 1q0 from China＇s sixth census have a serious data quality problem. The actual levels of 1q0 for each province are significantly higher than the reported values.展开更多
基金supported by a grant from the National Science Foundation of China:A Study on the Mortality Pattern of Chinese Population and Related Statistical Models(81273179)China’s sixth census excluds the data of Hong Kong SAR,Macao SAR,and Taiwan
文摘Objective To assess the data quality and estimate the provincial infant mortality rate（1q0） from China＇s sixth census. Methods A log-quadratic model is applied to under-fifteen data. We analyze and compare the average relative errors（AREs） for 1q0 between the estimated and reported values using the leave-one-out cross-validation method. Results For the sixth census, the AREs are more than 100% for almost all provinces. The estimated average 1q0 level for 31 provinces is 12.3‰ for males and 10.7‰ for females. Conclusion The data for the provincial 1q0 from China＇s sixth census have a serious data quality problem. The actual levels of 1q0 for each province are significantly higher than the reported values.
