• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.605-617
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• 2014

Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.109-123
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• 2008

Many small area estimation methods have been suggested. Also for the comparison of the estimation methods, model diagnostic checking techniques have been studied. Almost all of the small area estimators were developed by minimizing MSE(Mean square error) and so the MSE is the well-known comparison criterion for superiority. In this paper we suggested a new small area estimator based on minimizing MSPE(Mean square percentage error) which is recently re-highlighted. Also we compared the new suggested estimator with the estimators explained in Shin et al. (2007) using MSE, MSPE and other diagnostic checking criteria.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.321-328
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• 2009

In regression analysis, the ordinary least squares estimates of regression coefficients become poor, when the correlations among predictor variables are high. This phenomenon, which is called multicollinearity, causes serious problems in actual data analysis. To overcome this multicollinearity, many methods have been proposed. Ridge regression, shrinkage estimators and methods based on principal component analysis (PCA) such as principal component regression (PCR) and latent root regression (LRR). In the last decade, many statisticians discussed sensitivity analysis (SA) in ordinary multiple regression and same topic in PCR, LRR and logistic principal component regression (LPCR). In those methods PCA plays important role. Many statisticians discussed SA in PCA and related multivariate methods. We introduce the method of PCR and LRR. We also introduce the methods of SA in PCR and LRR, and discuss the properties of SA in PCR and LRR.

• Journal of Periodontal and Implant Science
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• v.43 no.6
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• pp.283-290
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• 2013

Purpose: The concept of gingival biotype has been used as a predictor of periodontal therapy outcomes since the 1980s. In the present study, prospective and controlled experiments were performed to compare periodontal pocket depth (PPD) reduction and gingival shrinkage (GSH) after scaling and root planing (SRP) according to gingival biotype. Methods: Twenty-five patients diagnosed with chronic periodontitis participated in the present study. The PPD and GSH of the labial side of the maxillary anterior teeth (from the right canine to the left canine) were evaluated at baseline and 3 months after SRP. Changes in the PPD following SRP were classified into 4 groups according to the gingival thickness and initial PPD. Two more groups representing normal gingival crevices were added in evaluation of the GSH. The results were statistically analyzed using the independent t-test. Results: In the end, 16 patients participated in the present study. With regard to PPD reduction, there were no significant differences according to gingival biotype (P>0.05). Likewise, sites with a PPD of over 3 mm failed to show any significant differences in the GSH (P>0.05). However, among the sites with a PPD of under 3 mm, those with the thin gingival biotype showed more GSH (P<0.05). Conclusions: PPD changes after SRP were not affected by gingival biotype with either shallow or deep periodontal pockets. GSH also showed equal outcomes in all the groups without normal gingival crevices. The results of SRP seem not to differ according to gingival biotype.