:: Volume 25, Issue 1 (1-2021) ::
Andishe 2021, 25(1): 101-109 Back to browse issues page
Nonresponse Prediction in an Establishment Survey Using Combination of Machine Learning Methods
Alireza Rezaee, Mojtaba Ganjali *, Ehsan Bahrami
Abstract:   (669 Views)
Nonrespose is a source of error in the survey results and National statistical organizations are always looking for ways to
control and reduce it. Predicting nonrespons sampling units in the survey before conducting the survey is one of the solutions
that can help a lot in reducing and treating the survey nonresponse. Recent advances in technology and the facilitation of
complex calculations have made it possible to apply machine learning methods, such as regression and classification trees
or support vector machines, to many issues, including predicting the nonresponse of sampling units in statistics. . In this
article, while reviewing the above methods, we will predict the nonresponse sampling units in a establishment survey using
them and we will show that the combination of the above methods is more accurate in predicting the correct nonresponse
than any of the methods.
Keywords: Classification and regression trees, logistic regression, nonresponse, Support vector machine
Full-Text [PDF 253 kb]   (204 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/08/10 | Accepted: 2021/01/20 | Published: 2021/01/29
References
1. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J., (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.
2. Cortez, C., and Vapnik, V., (1995). Support Vector Network, Machine learning. 20 (3), 273–297.
3. Durrant, Gabriele, B., and Steele, F., (2009). Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys. Journal of the Royal Statistical Society: series A (statistics in society), 172 (2), 361-381.
4. Earp, M., Mitchell, M., McCarthy, J. and Kreuter, F., (2014). Modeling Nonresponse in Establishment Surveys: Using an Ensemble Tree Model to Create Nonresponse Propensity Scores and Detect Potential Bias in an Agricultural Survey. Journal of Official Statistics, 30(4), 701–719.
5. Earp, M., Toth, D., Phipps, P. and Oslund, C., (2018). Assessing Nonresponse in a Longitudinal Establishment Survey Using Regression Trees. Journal of Official Statistics,34(2), 463–481.
6. Encyclopedia of Survey Research Methods (2008). published online 2011, edited by P.J. Lavrakas.
7. Hastie, T., Tibshirani, R. and Friedman, J., (2009). The Elements of Statistical Learning. 2th edition, Springer.
8. Kirchner, A., and Signorino, C S., (2018). Using Support Vector Machines for Survey Research.Survey Practice, 11(1).
9. Mitchell, T., M., (1997). Machine Learning. McGraw-Hill, p. 2.
10. Mohri, M., Rostamizadeh, A., and Talwalkar, A., (2018). Foundations of Machine Learning, 2th edition. The MIT Press.
11. Pham, B. and Prakash, I., (2018). Machine Learning Methods of Kernel Logistic Regression and Classification and Regression Trees for Landslide Susceptibility Assessment at Part of Himalayan Area, India. Indian Journal of Science and Technology, 11(12), 1-10.
12. Phipps, P. and Toth, D., (2012). Analyzing Establishment Nonresponse Using an Interpretable Regression Tree Model with Linked Administrative Data. The Annals of Applied Statistics, 6(2), 772-794.
13. Seiler, C., (2010). Dynamic Modelling of Nonresponse in Business Surveys. Ifo Working Paper No. 93.
14. Shuzhan. (2018). Understanding the mathematics behind Support Vector Machines. from https://shuzhanfan.github.io/2018/05/understanding-mathematics-behind-support-vector-machines/
15. Statistics Canada, (2020). Annual Survey of Manufacturing and Logging Industries (ASML), Detailed information for 2019. from https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey amp;SDDS=2103
16. U.S. Census Bureau, (2018). Annual Survey of Manufactures Methodology, from https://www.census.gov/programssurveys/asm/technical-documentation/methodology.html


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