TY - JFULL AU - Safaa R. Amer PY - 2007/1/ TI - Neural Network Imputation in Complex Survey Design T2 - International Journal of Computer and Information Engineering SP - 4036 EP - 4042 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/14932 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 12, 2007 N2 - Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design ER -