DOI QR코드

DOI QR Code

Imputation Using Factor Score Regression

  • Lee, Sang-Eun (Dept. of Applied Statistics, Kyonggi Univ.) ;
  • Hwang, Hee-Jin (Dept. of Statistics, Hankuk Univ. of Foreign Studies) ;
  • Shin, Key-Il (Dept. of Statistics, Hankuk Univ. of Foreign Studies)
  • Published : 2009.03.30

Abstract

Recently not even government polices but small town decisions are based on the survey data/information, so the most of government agencies/organizations demand various sample surveys in each fields for more detail information. However in conducting the sample survey, nonresponse problem rises very often and it becomes a major issue on judging the accuracy of survey. For that matters, one solution ran be using the administration data. However unfortunately most of administration data are restricted to the common users. The other solution can be the imputation. Therefore several method, of imputation are studied in various fields. In this study, in stead of the simple regression imputation method which is commonly used, factor score regression method is applied specially to the incomplete data which have the unit and item misting values in survey data. Here for simulation study, Consumer Expenditure Surveys in Korea are used.

References

  1. Alexander, B. (1993). Statistical Factor Analysis and Related Methods: Theory and Applications, John Wiley & Sons, New York
  2. Johnson, R. A. and Wichern, D. W. (2002). Applied Multivariate Statistical Analysis, Prentice-Hall International, New York
  3. Little, R. J. A. and Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons, New York
  4. Little. R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, John Wiley & Sons, New York
  5. Press, S. J. (1982). Applied Multivariate Analysis: Using Bayesian Frequentist Methods of Inference, Robert E. Krieger Publishing Company, Florida
  6. Press, S. J. (2003). Subjective and Objective Bayesian Statistics-Principles, Models, and Applica-tions, John Wiley & Sons, New York