• Journal of the Korean Statistical Society
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• v.1 no.1
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• pp.66-78
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• 1973

In Korea, the statistical system is very weak because it is not functional. Knowledge of statistical theory remains iosolated from applications: routine tasks of collection or processing of data are continued often without utilization, and programms are started in a superficial imitation of other without any purpose. It is essential, in Korea, to make statistics purposive. The only way is to give training statistics-fully developed technology of a multi-discipline character in applied statistics. The purpose of this study is primarily to survey the necessity of, or desire for, statistical tarining for the statistical personnel of the government agencies or bank offices in Seoul, Korea and discuss an adequate method of vacational training in statistics. This survey can be summarized as follows : (1) about 94 percent of the sampled people (478) do not consider their present statistical background adequately trained and 128 persons out of 478 request a graduate level training in respective fields. (2) The statistical fields on job in the sample are : Economic statistics : 138, Sampling survey : 228, management statistics : 50, other fields : 62. (3) Educational background are * College graduate : 369 (male 347, female 22) Economics 99, Business administration 99, Law 71, Mathematics and statistics 24, Others 76 * High school graduate : 109 (male 43, female 66)

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.407-416
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• 2001

Sample surveys are usually designed and analyzed to produce estimates for a large area or populations. Therefore, for the small area estimations, sample sizes are often not large enough to give adequate precision. Several small area estimation methods were proposed in recent years concerning with sample sizes. Here, we will compare simple Bayesian approach with Bayesian prediction for small area estimation based on linear regression model. The performance of the proposed method was evaluated through unemployment population data form Economic Active Population(EAP) Survey.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.593-600
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• 2007

Under repeated surveys, missing values often appear for various reasons and are replaced by new samples. It is investigated that the existing estimator in repeated survey by Jessen (1942), which has been originally developed for the new samples of fixed size, can be used in such situation where the size of new samples is random. It is shown that the proposed estimator has smaller variance than the sample mean.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.553-566
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper considers design-based estimators of the parameters of these variance functions based on the within-unit sample variances. This paper devotes to: (1) define an error scale factor $\delta$; (2) develop estimators of the parameters of the linear measurement error variance function of the true values under large-sample and small-error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U.S. Third National Health and Nutrition Examination Survey (NHANES III).

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.317-323
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• 2009

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.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.877-887
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• 2002

In this paper, behaviors and properties of p-values for goodness-of-fit test are investigated. With some findings on the p-values, we consider some applications to determine sample size of a survey research using the regression equation based on a pilot study data. Regression equations are obtained by the well-known least squared method, and we find that regression lines could be formulated with only two data points, alternatively. For further studies, this works might be extended to t distributions for testing hypotheses about population mean in order to determine sample size of a prospective study. Also similar arguments could be explored for F test statistics.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.29-44
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• 2000

In this paper, we consider one-level rotation designs with finite rotation groups such that the design satisfies two basic requirements: all rotation groups are included in any given survey period, and overlapping rates depend only on the time lag. First we present the necessary number of rotation groups and a rule for the length of time the sample units are to be in or out of the sample to satisfy the requirements. Second, an algorithm is presented to put rotation groups to proper positions in a panel in order to include all finite rotation groups for any survey period. Third, we define an one-level rotation pattern which is invariant in the survey period and has useful properties in practical sense.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.169-179
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• 2003

We suggest two types of misuses to analyze the same survey data. One is related with the fact that people nay use the wrong bounds of error when they compare two proportions. And the other is related with that some non-statisticians are apt to use wrong methods when there is a neutral answer in a question. We suggest these methods and compare them with the statistically good method. It will be a good results in educational purpose.

• Journal of the Korean Statistical Society
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• v.4 no.1
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• pp.19-33
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• 1975

The Ministry of Agriculture and Fishery had carried out a random sample survey on rice yield by the method of plot cutting since 1959 to obtain correct statistics on rice production. There are important problems connected with the determination of optimum size and shape of sample plot, variation of yield between plots and field and variation in the time needed for different types of plot cutting. This kind of research has been performed in India by Dr. P. V. Sukhatme and Dr. V. G. Panse. Also the same optimum procedures are much different in Japan that in India. In Korea, the optimum procedures may well be different from either of these countries. Although the government is proceeding with the crop yield survey, it does not attempt to investigate plot problems but emphasize total rice yield estimation. This particular research was conducted on a scale enough that the author could personally supervise all of the work.

• 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.