Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Weight Reduction Method for Outlier in Survey Sampling

  • Kim Jin (Regional Statistics & Sampling Division, Korea National Statistical Office)
  • Published : 2006.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Outliers in survey are a perennial problem for applied survey statisticians to estimate the total or mean of population. The influence of outliers is more increasing as they have large weights in survey sampling. Many techniques have been studied to lower the impact of outliers on sample survey estimates. Outliers can be downweighted by winsorization or reducing the weight of outliers. The weight reduction is more reasonable than replacing one outlier by one value of non-outliers, because it has at least one unit. In this paper, we suggest the square root transformation of weight as the weight reduction method. We show this method is efficient with real data, and it's also easy to apply in practical affairs.

Keywords

References

  1. Chambers, R.L. (1986). Outliers robust finite population estimation. Journal of Applied Statistics, Vol. 81, 1063-1069
  2. Cook, R.D. (1979). Influential observations in linear regression. Journal of Applied Statistics, Vol. 74, 169-174
  3. Gwet, J.P. and Rivest, L.P. (1992). Outlier resistant alternatives to the ratio estimator. Journal of Applied Statistics, Vol. 87, 1174-1182
  4. Hidiroglou, M.A. and Srinath, K.P. (1981). Some estimators of a population total from simple random samples containing large units. Journal of Applied Statistics, Vol. 76, 690-695
  5. Lee, H. (1995). Outliers in business surveys. Business Survey Methods. Chap. 26. 503-526
  6. Smith, T.M.F. (1987). Influential observations in survey sampling. Journal of Applied Statistics, Vol. 14, 143-152 https://doi.org/10.1080/02664768700000018
  7. Tukey, J.W. (1977). Exploratory Data Analysis, Addison-Wesley

Cited by

  1. Outlier detection and treatment in industrial sampling survey vol.27, pp.1, 2016, https://doi.org/10.7465/jkdi.2016.27.1.131
  2. Multiple Imputation Reducing Outlier Effect using Weight Adjustment Methods vol.26, pp.4, 2013, https://doi.org/10.5351/KJAS.2013.26.4.635