• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.111-119
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• 2002

One of the advantages of SP methods is the possibility of getting a number of responses from each respondent. However, when the repeated observations from each respondent are analysed by applying the simple modeling method, a potential problem is created because of upbiased significance due to the repeated observation from each respondent. This study uses a variety of approaches to explore this issue and to test the robustness of the simple model estimates. Among several different approaches, the Jackknife method and Kocurs method were applied. The Jackknife method was implemented using a program JACKKNIFE. The model estimate results of Jackknife method and Kocurs method were compared with those of the uncorrected estimates in order to test whether there was repeated measurement problem or not and the extent to which this problem affected the model estimates. The standard errors between the uncorrected model estimates and Jackknife estimates were also compared. The results reveals that the t-ratios of Kocurs are much lower than those of the uncorrected method and Jackknife estimates, indicating that Kocurs method underestimates the significance of the coefficients. Jackknife method produced the almost same coefficients as those of the uncorrected model but the lower t-ratios. These results indicate that the coefficients of the uncorrected method are accurate but that their significance are somewhat overestimated. In this study. 1 concluded that the repeated measurement Problem did exist in our data, but that it did not affect the model estimation results significantly. It is recommended that such a test should become a standard procedure. If it turns out that the analysis based on the simple uncorrected method are influenced by the repeated measurement problem. it should be corrected.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.155-160
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• 2004

불균등 확률 계통추출에서는 모집단 총합에 대한 Horvitz-Thompson 추정량의 대안적 분산 추정량들을 사용하게 된다. 이와 같은 모총합에 관한 분산 추정량들의 설계와 관련한 일반적인 방법은 균등 확률 계통추출에 대한 분산 추정량들에서 시작하고 비율 $y_i,/P_i$에 의한 추정량의 정의에서 $y_i$를 재배치하게 한다. 비선형 조사 통계학에서 추정량들 중의 하나로 테일러 급수 공식을 적용한다. 불균등 확률 계통추출에서의 분산은 8가지 방법으로 추정이 가능하므로 이를 이용한 분산추정량을 구해보고, 비복원 불균등 확률에서의 jackknife방법을 살펴보고자 한다. 또한 이들 분산추정량들에 대한 비교를 몇 가지 방법을 이용하여 알아보도록 한다.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.643-648
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• 2012

There are lots of analysis to determine the relation between dependent variable and explanatory variables. Often the regression analysis is used to do this, and we can analyze the how much the explanatory variable can be related with dependent variable and how much the regression model can explain the data. But the validation check of regression model is usually determined by coefficient of determination. We should check the validation of regression coefficient with different methods. This paper introduces the method for validation check the regression coefficient using the jackknife regression and bootstrap regression in clinical data.

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.857-866
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• 2009

In this study, basic data is consisted annual maximum rainfall at 56 stations that has the rainfall records more than 30years in Korea. The 14 probability distributions which has been widely used in hydrologic frequency analysis are applied to the basic data. The method of moments, method of maximum likelihood and probability weighted moments method are used to estimate the parameters. And 4-tests (chi-square test, Kolmogorov-Smirnov test, Cramer von Mises test, probability plot correlation coefficient (PPCC) test) are used to determine the goodness of fit of probability distributions. This study emphasizes the necessity for considering the variability of the estimate of T-year event in hydrologic frequency analysis and proposes a framework for evaluating probability distribution models. The variability (or estimation error) of T-year event is used as a criterion for model evaluation as well as three goodness of fit criteria (SLSC, MLL, and AIC) in the framework. The Jackknife method plays a important role in estimating the variability. For the annual maxima of rainfall at 56 stations, the Gumble distribution is regarded as the best one among probability distribution models with two or three parameters.

• Korean Journal of Plant Taxonomy
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• v.36 no.2
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• pp.91-108
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• 2006

Phylogenetic analyses were conducted to evaluate evolution and relationship of 16 taxa of Korean Geranium including 3 outgroups using ITS (internal transcribed spacer) squences of nuclear ribosomal DNA. Phylogenetic studies used most parsimony and neighbor-joining methods including bootstrapping and jackknifing analysis. As the result, Korean Geranium forms monophyletic group. In the parsimony tree G. koraiense var. hallasanense situated as the most basal clade and Erianthum group forms one clade by high bootstrap ans jackknife values (100% of bootstrap and jackknife values). G.dahuricum as one of the Krameri group is closely related with Palustre group by very weak relationship (37% of bootstrap and 44% of jackknife values) and the node collapse in the strict tree. G. Knuthii which was one of wilfordii group is closely related with Koreanum group. G. sibiricum, one of Sibiricum group, is the most closest relationship with G. soboliferum and these species are sister to G. krameri. G. tripartitum and G. wilfordii which are wilfordii group are linked to G. nepalense, G. thunbergii f. pallidum and G. thunbergii. This result suggested that the phylogenetic analysis of ITS sequences should be useful to address phylogenetic questions on the genus Korean Geranium.

• Survey Research
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• v.10 no.1
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• pp.141-154
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• 2009

With the current sampling scheme, the sampling variance is getting larger in producing smaller regional statistics than the designed area, The larger sample size can make the variance reduced but the efficiency of sample survey lower. The desired confidence level of sampling survey can be obtained using the current sample scheme with the same sample size and administrative data. In this paper, the number of the unemployed of 5 regions in Daejon are estimated using small area estimation methods and the CV values in each estimation method is calculated and compared for their estimation efficiency as empirical study. Jackknife method is proposed to estimate the MSE of synthetic estimator and composite estimator more accurately.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.349-356
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• 2009

In this study, we suggest the estimation method of the relative risk for the unemployment statistics of a small area such as si, gun, gu in Korea. The considered method are the usual pooled estimator, weighted estimator with the inverse of log-variance as weights, and the Jackknife estimator. And we compare with the efficiency of the three estimators by estimating the bias and mean square errors using real data from the 2002 Economically Active Population Survey of Gyeonggi-do. We compute the unemployed rate of male and female in small areas, and then estimate the common relative risk for the unemployed rate between male and female. Also, the stability and reliability of the three estimators for the common relative risk was evaluated using the RB(relative bias) and the RRMSE(relative root mean square error) of these estimators. Finally, the Jackknife estimator turned out to be much more efficient than the other estimators.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.91-105
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• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.311-328
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• 2005

This paper deals with a treatment of nonresponse in farm and fishery household economy surveys in Korea. Since the samples in two surveys were selected by stratified multi-stage sampling and weighted sample means has been used to estimate the population means, we choose a weighted hot-deck imputation method as an appropriate method for two surveys. We investigate the procedure of the weighted hot-deck as well as an adjusted jackknife method for variance estimation. Through an empirical study we found that the method worked very well in both mean and variance estimation in two surveys. In addition, we presented a procedure of forming imputation class and formed four imputation classes for each survey and then compared them with analysis. As a result, we presented two most efficient imputation classes for two surveys.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.11a
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• pp.77-85
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• 2001

In this paper we study the calibration estimator and its variance estimator for the population total using a bootstrap method according to the levels of an auxiliary information having strong correlation with an interested variable in nonresponse situation. At this point, we find tire calibration estimator in case of auxiliary information for population and sample, and then we drive the bootstrap variance estimator of it. By simulation study we compare the efficiencies with the Taylor and Jackknife variance estimators.