• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.375-382
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• 2010

In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1115-1128
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• 2011

Cut-off sampling that discards a part of the population from the sampling frame, is a widely used method for a highly skewed population like a business survey. Usually to the estimate of population total, we need to estimate the total of the take-nothing stratum. Many estimators have been developed to estimate the total of the take-nothing stratum. In this paper, we suggest a new composite estimator which combines the estimator suggested by Sarndal et al. (1992) and a ratio estimator obtained by small samples from the take-nothing stratum. Small simulation studies are performed for the comparison of the estimators and we confirm that the new suggested estimator is superior to the others.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1235-1243
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• 2010

This study proposes a trimmed LAD(least absolute deviation) estimators for multi-dimensional contingency tables and suggests an algorithm to estimate it. In addition, a method to determine the trimming quantity of the estimators is suggested. A Monte Carlo study shows that the propose method yields a better trimming rate and coverage rate than the previously suggest method based on the determinant of the covariance matrix.

• Survey Research
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• v.5 no.1
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• pp.93-101
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• 2004

In business survey, cut-off sampling is usual, The contribution from cut-off part of the population is at least small in comparison with the remaining population. In this case, part of the target population is excluded from the selection and parameter estimations are only based on Take-all and Take-some stratum. It may be tempting not to use resources on enterprises that contribute little to the overall results of the survey. And this reduces the response burden for these small enterprises. But, the size of cut-off stratum has been increased as a way to manage reduced budgets. This leads to additional bias. In this study, the population have been separated as three stratum, cut -off, take-some, take-all, and we will estimate cut-off part using auxiliary variable.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.13-22
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• 2012

Cut-off sampling that discards a part of the population from the sampling frame, is a widely used method for a business survey. Usually, to the estimate of population total, an accurate estimate of the total of the take-nothing stratum is required. Many estimators have been developed to estimate the total of the take-nothing stratum. Recently Kim and Shin (2011) suggested a composite estimator and showed the superiority of that estimator. In this paper, we suggest an alternative composite estimator obtained by combining BLUP estimator and a ratio estimator obtained by the small samples from the take-nothing stratum. Small simulation studies are performed for a comparison of the estimators and we confirm that the new suggested estimator is superior.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.29-33
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• 2003

In business survey. cut off sampling is usual. The contribution from cut off part of the population is at least small in comparison with the remaining population. In this study. the population have been separated as three stratum, cut-off. take-some, take-all, and we will estimate cut-off part.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.43-59
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• 2014

Cut-off sampling has been widely used for a highly skewed population like a business survey by discarding a part of the population, so called a take-nothing stratum. For a more accurate estimate of the population total, Hwang and Shin (2013) suggested a composite estimator of a take-nothing stratum total that combined the survey results of a take-nothing stratum and a take-some sub-stratum (a part of take-some stratum). In this paper we propose a new cut-off sampling scheme by considering a cost function and a composite estimator based on the proposed sampling scheme. Small simulation studies compared the performances of known composite estimators and the new composite estimator suggested in this study. We also use Briquette Consumption Survey data for real data analysis.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.1-11
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• 1996

As an estimate of a location parameter for a given data set, $\alpha$-trimmed mean has been studied for a long time by many statisticians because of its nice propoerties including robustness. However, its performance depends on the proportion of trimming say $\alpha$. In this paper, we suggest a data-driven choice of $\alpha$ and study its validity. Also, we suggest a new estimator and consider double-bootstrap to improve its performance. By using simulation study, the proposed method is compared with the exiting one in various cases. Real data sets are also analyzed by using the proposed method.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.173-182
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• 1994

By applying slope estimator under the arbitrary error distributions proposed by Han(1993), if we define regression quantiles to give upper and lower trimming part and blocks of data, we show the proposed slope estimator has asymptotically efficient slope estimator when the number of regression quantiles to from blocks of data goes to sufficiently large.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.111-120
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• 2001

Either systematic sampling or stratified sampling is usually applied to the business conditions survey when companies don't have much difference in their size. But the cutoff systematic sampling is an efficient method when only a few companies are so large that the total of them almost equals to the total of whole companies. Throughout this paper, three estimators of total and their variance estimations depending on three kinds of sampling schemes are discussed, and are compared with them via their variances. It is proved that the cut-off systematic sampling is most efficient by using a real data of the logging business conditions survey.