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Activity Number: 508 - Leading the Estimates Towards Known Benchmarks
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #328770 Presentation
Title: Deep Learning for Data Imputation and Calibration Weighting
Author(s): Yijun Wei* and Luca Sartore and Jake Abernethy and Darcy Miller and Kelly Toppin and Clifford Spiegelman and Michael Hyman
Companies: NISS and National Institute of Statistical Sciences and National Agricultural Statistics Service, United States Department of Agriculture and National Agricultural Statistics Service and National Agricultural Statistics Service and Texas A&M University and USDA-NASS
Keywords: Imputation; Neural network model; NASS survey data
Abstract:

The USDA's National Agricultural Statistics Service (NASS) surveys are affected by nonresponse and by incomplete responses that may not be homogeneous across farm types and sizes. To address item nonresponse, NASS employs a variety of imputation methods such as ratio imputation, iterative sequential regression, fully conditional specification, K-nearest neighbor, carry forward of previously reported data and manual imputation to provide reliable and consistent values on NASS data. To address unit nonresponse and some other sources of error, NASS currently uses a set of generalized linear regression models to estimate the number of US farms by calibrating their corresponding weights. However, linear models cannot always capture important nonlinear features of the population. Deep learning (including artificial neural network) models are used successfully in numerous other applications in order to capture nonlinear properties efficiently. In this paper, imputation techniques and the adjustment of the survey weights are integrated. A potential unified deep learning method simultaneously adjust survey weights and impute missing values is discussed.


Authors who are presenting talks have a * after their name.

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