• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.233-246
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• 1998

We made a sample design for next 5 years, based on the population as of 1995, for livestock statistics. In the sample design, we used the stratified one stage sampling method where the sample size depends on the prefixed coefficient of variation. In stratifying the population, we considered the complete linkage method, and decided the number of strata to be the one which yields the minimum sample size. We listed here some difficulties we had for the better sample design in the future.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.13-15
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• 2005

In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.451-460
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• 2008

This paper reviews nonparametric statistics by Neyman-Pearson test and Fisher test. Nonparametric statistics deal with the small sample with distribution-free assumption in multi-product and small-volume production. Two tests for various nonparametric statistic methods such as sign test, Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Mood test, Friedman test and run test are also presented with the steps for testing hypotheses and test of significance.

• Journal of the Korean Statistical Society
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• v.22 no.1
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• pp.13-25
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• 1993

The purpose of this paper is to investigate the properties of order statistics under various stochastic relations. We study the stochastic comparison of order statistics in a single sample. And we consider two sample case too. For example, F(t) > G9t) for t > 0 when X and Y are random variables symmetric about 0, with c.d.f.s F and G. Two examples are provided.

• Journal of Nutrition and Health
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• v.45 no.6
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• pp.600-612
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• 2012

Nutritional researchers world-wide are using large-scale sample survey methods to study nutritional health epidemiology and services utilization in general, non-clinical populations. This article provides a review of important statistical methods and software that apply to descriptive and multivariate analysis of data collected in sample surveys, such as national health and nutrition examination survey. A comparative data analysis of the Korea National Health and Nutrition Examination Survey (KNHANES) was used to illustrate analytical procedures and design effects for survey estimates of population statistics, model parameters, and test statistics. This article focused on the following points, method of approach to analyze of the sample survey data, right software tools available to perform these analyses, and correct survey analysis methods important to interpretation of survey data. It addresses the question of approaches to analysis of complex sample survey data. The latest developments in software tools for analysis of complex sample survey data are covered, and empirical examples are presented that illustrate the impact of survey sample design effects on the parameter estimates, test statistics, and significance probabilities (p values) for univariate and multivariate analyses.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.185-195
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• 2003

Recently, the importance of livestock statistics is increasing because of the food consumption pattern in Korea is changing. We compare the old sample design based on the 1995 National Agriculture Census with the new sample design based on the 2000 National Agriculture Census. We present some considerations to improve the efficiency of the sample design in livestock sector survey.

• Journal of applied mathematics & informatics
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• v.5 no.1
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• pp.263-268
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• 1998

This paper proposes the two-sample comparison us-ing sign test based on ranked-set sample(RSS). We investigate the asymptotic properties of the proposed test statistic and compare the asymptotic relative efficiencies of the proposed test statistic with re-spect to Mann-Whitney-Wilcoxon test statistic based on RSS and Mann-Whitney-Wilcoxon test statistic based on the simple random sample(SRS).

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.215-226
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• 1997

The purpose of the labor statistics survey is to collect materials on employment, wages and the working time and to analyze the trend of the labor situation. in this research, the stratification variables are industry and the size of establishment. The sample are selected by stratified one stage sampling method in order to produce the reliable estimates of labor statistics. For local labor statistics, we design the sample survey using the city and province as sub-population. So we are able to produce the local area estimates of labor statistics with respect to industry and the size of establishment.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.605-618
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• 2007

Adaptive sampling design is the selection procedure which depends on observed values of the variable of interest. It is the method which could be applied to the rare and unapproachable population. Adaptive cluster sampling strategies are more efficient than simple random sampling on equivalent sample size. Adaptive sampling with new estimators through the Rao-blackwell method have lower variance than Horvitz-Thompson (HT) and Hansen-Hurwitz (HH). Also, to determine suitable sample size, it was used expected sample and the method finding appropriate sample size by changing initial sample size were studied.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.733-749
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• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.