Safaa R. Amer
Neural Network Imputation in Complex Survey Design
4037 - 4042
2007
1
12
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/14932
https://publications.waset.org/vol/12
World Academy of Science, Engineering and Technology
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 publicuse dataset was used as an example to illustrate neural networks imputation under a complex survey design
Open Science Index 12, 2007