• Journal for History of Mathematics
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• v.11 no.2
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• pp.63-76
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• 1998

When teaching the linear regression analysis in the class, the power transformation must be introduced to fit the linear regression model for nonlinear data. Box and Cox (1964) proposed the attractive power transformation technique which is so called Box-Cox transformation. In this paper, an effective algorithm selecting an appropriate value for Box-Cox transformation is developed which is considered to find a value minimizing error sum of squares. When the proposed algorithm is used to find a value for transformation, the number of iterations needs to be considered. Thus, the number of iterations is examined through simulation study. Since SAS is one of most widely used packages and does not provide the procedure that performs iterative Box-Cox transformation, a SAS program automatically choosing the value for transformation is developed. Hence, the students could learn how the Box-Cox transformation works, moreover, researchers can use this for analysis of data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.967-976
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• 2011

The Box-Cox transformation is a well known family of power transformations that brings a set of data into agreement with the normality assumption of the residuals and hence the response variable of a postulated model in regression analysis. Normalization (studentization) of the regressors is a common practice in analyzing microarray data. Here, we implement Box-Cox transformation in normalizing regressors in microarray data. Pridictabilty of the model can be improved using data transformation compared to studentization.

• Journal of Preventive Medicine and Public Health
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• v.43 no.6
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• pp.543-548
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• 2010

Objectives: The objective of this study was to calculate sample size and power in an ongoing cohort, Korea radiation effect and epidemiology cohort (KREEC). Method: Sample size calculation was performed using PASS 2002 based on Cox regression and Poisson regression models. Person-year was calculated by using data from '1993-1997 Total cancer incidence by sex and age, Seoul' and Korean statistical informative service. Results: With the assumption of relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, sample size calculation was 405 events based on a Cox regression model. When the relative risk was assumed to be 1.5 then number of events was 170. Based on a Poisson regression model, relative risk=1.3, exposure:non-exposure=1:2 and power=0.8 rendered 385 events. Relative risk of 1.5 resulted in a total of 157 events. We calculated person-years (PY) with event numbers and cancer incidence rate in the nonexposure group. Based on a Cox regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, 136 245PY was needed to secure the power. In a Poisson regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, person-year needed was 129517PY. A total of 1939 cases were identified in KREEC until December 2007. Conclusions: A retrospective power calculation in an ongoing study might be biased by the data. Prospective power calculation should be carried out based on various assumptions prior to the study.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.233-250
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• 2002

Cox's proportional hazard model is highly-used for the regression analysis of survival data in various fields. Regression diagnostics for the proportional hazards model, however, is not as well-known as the diagnostics for the classical linear models and so these diagnostic methods are not used widely in our practical data analyses. For this reason, we review the residuals proposed by several authors, and investigate how to use them in assessing the model. We also provide the results and interpretation with the analysis of PBC data using S-plus 2000 program.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.143-150
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• 2006

Box-Cox model and T-factor method have been widely used to measure economic depreciations for industrial property. The Box-Cox model which combines economic efficiency with depreciation pattern is here extended to the reliability function. To do so a Rayleigh distribution which has been used to estimate the reliability of current assets was chosen as an efficiency curve of marginal productivity. Such an approach provides the possibility to classify the efficiency curves into four categories. It is also possible to analyze the types of depreciation curves. Therefore, the power family of a non-linear Box-Cox model could be set at certain constant values, then the model can be transformed into a linear model to estimate the economic depreciation rates by utilizing the reliability function. Estimating the resultant linear regression equation requires minimal number of observations, while at the same time facilitating the test of hypothesis on depreciation rates.

• Korean Journal of Social Welfare
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• v.61 no.3
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• pp.307-328
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• 2009

This study aims to investigate the patterns and causes of the marital duration. Data used for this study are ten waves of Korean Labor and Income Panel Study(KLIPS) in 1998~2007, in which the final sample consists of 2,397 households. The Life-table method is used for describing the overall patterns of marital duration by birth-cohorts and different education groups, and the Cox proportional hazard regression model is used to identify significant factors on the marital duration. The results show that among the all respondents, the 0.79% has divorced or separated within five years after marriage, 2.12% within 10 years, and 5.84% within 20 years, respectively. In addition, the Cox regression results show that the marital duration is significantly affected by the birth-cohorts of respondents and their spouses, education level, earning of spouses, co-residence with parents, and household income. This implies that the hazard rate of marital disruption is higher for younger cohorts, individuals with lower education and economic status, persons living with parents-in-law, compared to their counterparts. Thus, it is necessary to implement social welfare policies applicable for these persons.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.251-255
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• 2005

중도절단된 자료와 표본수가 적은 자료를 가지는 생존분석에서 생존율을 추정하거나 두 집단의 생존율을 비교할 때 정규분포 근사를 가정한 신뢰구간을 이용하는 데는 많은 어려움이 생긴다. 생존함수의 신뢰구간에 대한 중도절단을, 표본의 크기에 따른 다양한 상황의 모의실험을 통하여 Kaplan-Meier, Nelson, 적률 추정량 그리고 cox model의 ${\beta}$을 가지고 붓스트랩을 이용한 신뢰구간과 비모수 신뢰구간, 우도비 신뢰구간의 실제 포함 확률을 비교해보고자 한다.

• Journal of Chest Surgery
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• v.42 no.6
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• pp.710-718
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• 2009

Background: The Cox maze procedure has been used as a standard surgical treatment for atrial fibrillation for about 20 years. Recently, the creators have used a bipolar radiofrequency electrode (Cox maze IV procedure) instead of the incision and suture (cut-sew) technique to make atrial ablation lesions for persistent atrial fibrillation. We investigated clinical outcomes for the Cox maze procedure with a bipolar radiofrequency electrode and cryoablator in patients with persistent atrial fibrillation, and compared results with clinical outcomes of the cut-sew procedure. Material and Method: Between April 2005 and July 2007, 40 patients with persistent atrial fibrillation underwent Cox maze IV procedure with a bipolar radiofrequency electrode and cryoablator (bipolar radiofrequency group). Surgical outcomes were compared with those of 35 patients who had the cut-sew technique for the Cox maze III procedure. All patients had concomitant cardiac surgery. Postoperatively, the patients were followed up every 1 to 2 months. Result: At 6 months postoperatively, the conversion rate to regular sinus rhythm was not significantly different between the two groups: 95.0% for the bipolar radiofrequency ablation group; 97.1% for the cut-sew technique (p=1.0). At the end of the follow-up period, the conversion rate to regular sinus rhythm was also not significantly different (92.5% vs. 91.6%, p=1.0). In multivariate analysis using a Cox-regression model, the postoperative atrial dimension was an independent determinant of sinus conversion in the bipolar radiofrequency ablation group (hazard ratio 31, p=0.005). In the Cox-regression model for both groups, atrial fibrillation at 6 months postoperatively (hazard ratio 92.24, p=0.003) and the postoperative left atrial dimension (hazard ratio 16.05, p=0.019) were independent risk factors of continuance or recurrence of atrial fibrillation after Cox maze procedures. Aortic cross-clamp time and cardiopulmonary bypass time were significantly shorter in the radiofrequency group than in the cut-sew group. Conclusion: In the Cox maze procedure for patients with persistent atrial fibrillation, the use of bipolar radiofrequency ablation and a cryoablator is as good as the cut-sew technique for conversion to sinus rhythm. The postoperative left atrial dimension is an independent determinant of postoperative continuance and recurrence of atrial fibrillation.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.