• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.19-27
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• 2006

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.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.119-131
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• 1995

Based on Monte-Carlo simulation results we propose a robust analysis of variance procedure by utilizing trimmed mean and Winsorized variance. We deal with mainly the one-way classification case. We evaluate the empirical distribution of a pseudo-F statistic based on symmetrically Winsorized sum of squares when the population is normally distributed.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.131-142
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• 2016

Outliers in surveys can have a large effect on estimates of totals. This is especially true in business surveys where the populations are drawn are typically skewed. In this paper, we discussed the practical development and implementation of methods to identify and deal with outliers. A detection method is based on quartile method and detected outlier is processed in various ways. The study examines two versions of winsorised estimators with three different cut-off thresholds for each one. For the simulation study, four types of weight transformation function have been considered.