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Activity Number: 243
Type: Contributed
Date/Time: Tuesday, August 8, 2006 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #306897
Title: Nonlinear Neural Network Imputation
Author(s): Safaa Amer*+
Companies: National Opinion Research Center
Address: 946 NW Circle Blvd., Corvallis, OR, 97330,
Keywords: imputation ; missing data ; neural networks ; non-linear
Abstract:

Non-linear models offer a flexible realistic way of imputing missing data. An evaluation of the performance of a class of feed-forward non-linear neural networks in imputation is presented compared to other statistical techniques. Results show that neural network imputation technique offers a tremendous speed advantage and similar or better performance relative to some other statistical imputation techniques.

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