• Journal of Dental Rehabilitation and Applied Science
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• v.29 no.3
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• pp.308-316
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• 2013

The purpose of this study is to investigate the production method of posterior bridges pontic ridge lap type which prevents the infection in bridge pontic base and is able to cleanse itself, in the process of producing final prothesis that maintains healthy mucous membrane of oral cavity and interproximal papilla, minimizing diastema, is aesthetic and has no effect on pronunciation. New technology is applied to make optimal pontic base which prevent inflammation and clean itself and its products were clinically evaluated in 10 places of dental clinics in busan and gyeongnam. The making of posterior 3 unit bridge pontic base, it was presented as the new technology of forming ridge lab type and to carry out clinical validation, existing conventional method and the new technology were compared. Pontic base made with the existing conventional method cause infection and other periodontal disease by 96% but the pontic base made with the new technology cause infection and other periodontal disease by 3%. Remains of food cause infection and other periodontal disease 100% by the existing conventional method and 91% by the new technology, showing a distinct difference. However, after a gargle, the new technology had low 13%. Additionally, the pontic base made with the existing conventional method showed 71% of chance, the new technology method showed 8% of chance in terms of self-cleansing.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.899-907
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• 2010

Several non-response imputation methods are suggested, however, mainly cross-sectional imputations are studied and applied to this analysis. A simple and common imputation method for panel data is the cross-wave regression imputation or carry-over imputation as a special case of cross-wave regression imputation. This study suggests a multiple imputation method combined time series analysis and cross-sectional multiple imputation method. We compare this method and the cross-wave regression imputation method using MSE, MAE, and Bias. The 2008 monthly labor survey data is used for this study.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1229-1241
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• 2014

Most of sampling surveys have outliers and non-response missing values simultaneously. In that case, due to the effect of outliers, the result of imputation is not good enough to meet a given precision. To overcome this situation, outlier treatment should be conducted before imputation. In this paper in order for reducing the effect of outlier, we study outlier imputation methods and outlier weight adjustment methods. For the outlier detection, the method suggested by She and Owen (2011) is used. A small simulation study is conducted and for real data analysis, Monthly Labor Statistic and Briquette Consumption Survey Data are used.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.635-647
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• 2013

Imputation is a commonly used method to handle missing survey data. The performance of the imputation method is influenced by various factors, especially an outlier. The removal of the outlier in a data set is a simple and effective approach to reduce the effect of an outlier. In this paper in order to improve the precision of multiple imputation, we study a imputation method which reduces the effect of outlier using various weight adjustment methods that include the removal of an outlier method. The regression method in PROC/MI in SAS is used for multiple imputation and the obtained final adjusted weight is used as a weight variable to obtain the imputed values. Simulation studies compared the performance of various weight adjustment methods and Monthly Labor Statistic data is used for real data analysis.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.909-921
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• 2010

Cross-wave regression imputation and carry-over imputation method are generally used in the analysis of panel data with missing values. Recently it is known that the BLS non-response adjust method has good statistical properties. In this paper we show that the BLS method can be considered as an imputation method with a similar formula of a ratio-estimator. In addition, we show that the carry-over imputation and BLS imputation are approximately the same under the assumption that data follow a non-stationary process with drift. Small simulation studies and real data analysis are performed. For the real data analysis, a monthly labor statistic (2007) is used.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.367-376
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• 2011

BLS weight adjustment is a widely used method for business surveys with non-responses and outliers. Recent surveys show that the non-response weight adjustment of the BLS method is the same as the ratio imputation method. In this paper, we suggested a modified BLS weight adjustment method by imputing missing values instead of using weight adjustment for non-response. Monthly labor survey data is used for a small Monte-Carlo simulation and we conclude that the suggested method is superior to the original BLS weight adjustment method.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1998.05a
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• pp.69-72
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• 1998

지속적인 경제성장과 산업발달과 더불어 에너지 소비량이 크게 증가하고 있고, 환경문제가 심각해지고 있다. 이에 따라 대기로 배출되는 질소산화물은 산성비 및 도심스모그의 주범이 되는 물질로서, 그 미치는 파장이 사회적으로 매우 크다. 이러한 질소산화물을 제거하는 방법으로서, 기존의 선택적 촉매 및 비촉매 환원법은 고온을 필요로 하므로, 설치 및 운전비가 많이 요구되는 방법들을 대체하기 위해 상온영역에서 조업되는 광촉매를 개발해서, 신기술을 확립하고, 환경규제에 대해 능동적으로 대처하여야 한다. 기존의 탈질공정에서는 부가적인 에너지가 필요하므로, 광촉매를 통한 질소산화물의 저감기술은 에너지 소비가 작다는 장점이 있다. (중략)

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.485-499
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• 2022

Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.

• Environmental and Resource Economics Review
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• v.21 no.3
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• pp.595-625
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• 2012

Recently environment-friendly pellet boilers have interests as emissions of greenhouse gases are regulated internationally and energy security becomes more important to oil addicted countries including Republic of Korea. But the Korean market for pellet boilers is on the initial stage due to the high production costs relative to other conventional boilers. Hence the Korean government has supported financially and promoted the pellet boiler business. In this sense, it would contribute market stratergy and effective promotion policy for both of the government and private companies if we can forecast market shares of pellet boilers appropriately. For this purpose, this study surveyed potential consumers' preferences on pellet boilers among various alternatives using a choice experiment reflecting intangible costs. As the market share of new technology increases, intangible costs decline. According to different intangible cost scenarios, we experimented people's preferences on oil, gas, electric, and pellet boilers. A multinomial logit model was employed to estimate coefficient parameters of common attributes for various alternative boilers. Based on the estimates, we forecasted market shares of individual boilers. We found that as intangible costs decline, the market share of pellet boiler increase substantically while market shares of electric and gas boilers decrease dramatically. The market share of oil boiler did not change significantly. Meanwhile, as people are more rich, more educated, and exposed to advertisement on pellet boilers, the likelihood of choosing the pellet boiler increases.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.255-264
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• 2008

It is common in practice to use mean squared error(MSE) for prediction. Recently, Park and Shin (2005) and Jones et al. (2007) studied prediction based on mean squared relative error(MSRE). We proposed a new nonparametric way of prediction based on MSRE substituting Jones et al. (2007) and provided a small simulation study which highly supports the proposed method.