• Journal of Teachers' Pension
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• v.3
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• pp.279-305
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• 2018

공적연금제도는 장기적 유지 및 운영을 위해 기금의 재정건전성 및 지속가능성 진단을 목적으로 재정계산제도를 운영하고 있다. 정확한 재정계산은 매우 중요하며 이를 위한 선행작업으로 재정계산에 요구되는 기본 가정들을 보다 합리적으로 추정해야 할 필요가 있다. 본 연구는 로지스틱 회귀분석(logistic regression)을 이용하여 사학연금의 재정계산에 적용되는 다양한 기초율들 중 퇴직률을 산출하는 것에 그 목적이 있다. 사학연금은 현재 퇴직률을 교원 및 직원에 대하여 각 성별로 총 4개 집단을 구분하여 각 집단별 가입연령과 재직기간에 따라 산출하고 있다. 그러나 본 연구에서는 학교급 등 퇴직률 산출에 있어 보다 유의한 집단 구분이 있는지를 확인하고 보정의 어려움을 피할 수 있는 하나의 대안으로서 로지스틱 회귀분석을 이용하여 퇴직률을 산출해 보았다. 또한 우수한 모형을 판별하기 위해 통계적으로 우수한 모형보다는 실무적으로 사학연금 재정추계에 적합한 모형을 찾는 것을 목표로 하여 퇴직률을 추정한 값을 제시하였다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.19-26
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• 2014

In this paper, we propose a polychotomous regression model when independent variables include both categorical and numerical variables. For categorical independent variables, we use direct sums, and tensor product splines are used for continuous independent variables. We use BIC for varible selections criterior. We implemented the algorithm and apply the algorithm to real data. The use of direct sums and tensor products outperformed the usual multinomial logistic regression model.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.482-490
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• 2017

This study is a railway accident investigation statistic study with the purpose of prediction and classification of accident severity. Linear regression models have some difficulties in classifying accident severity, but a logistic regression model can be used to overcome the weaknesses of linear regression models. The logistic regression model is applied to escalator (E/S) accidents in all stations on 5~8 lines of the Seoul Metro, using data mining techniques such as logistic regression analysis. The forecasting variables of E/S accidents in urban railway stations are considered, such as passenger age, drinking, overall situation, behavior, and handrail grip. In the overall accuracy analysis, the logistic regression accuracy is explained 76.7%. According to the results of this analysis, it has been confirmed that the accuracy and the level of significance of the logistic regression analysis make it a useful data mining technique to establish an accident severity prediction model for urban railway casualty accidents.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.269-279
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• 2015

This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.857-870
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• 2010

This study was conducted to investigate the relationship between the socio-demographic factors and the Korean consumers palatability evaluation grades with Hanwoo sensory evaluation data from 2006 to 2008 by National Institute of Animal Science. The dichotomy logistic regression model and the multinomial logistic regression model are fitted with the independent variables such as the consumer living location, age, gender occupation, monthly income, beef cut and the the palatability grade as the categorical dependent variable and tenderness, 리avor and juiciness as the continuous dependent variable. Stepwise variable selection procedure is incorporated to find the final model and odds ratios are calculated to nd the associations between categories.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.1-11
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• 2012

We present methods to study the log-density ratio of the conditional densities of the predictors given the response variable in the logistic regression model. This allows us to select which predictors are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. A simulation study shows that the linear and log terms are required in general. If the conditional distributions of xjy for the two groups overlap significantly, we need both the linear and log terms; however, only the linear or log term is needed in the model if they are well separated.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.829-839
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• 2013

This study is a descriptive research study with the purpose of predicting and comparing factors of depression affecting residents in a metropolitan city by using logistic regression analysis and decision-making tree analysis. The subjects for the study were 462 residents ($20{\leq}aged{\angle}65$) in a metropolitan city. This study collected data between October 7, 2011 and October 21, 2011 and analyzed them with frequency analysis, percentage, the mean and standard deviation, ${\chi}^2$-test, t-test, logistic regression analysis, roc curve, and a decision-making tree by using SPSS 18.0 program. The common predicting variables of depression in community residents were social dysfunction, perceived physical symptom, and family support. The specialty and sensitivity of logistic regression explained 93.8% and 42.5%. The receiver operating characteristic (roc) curve was used to determine an optimal model. The AUC (area under the curve) was .84. Roc curve was found to be statistically significant (p=<.001). The specialty and sensitivity of decision-making tree analysis were 98.3% and 20.8% respectively. As for the whole classification accuracy, the logistic regression explained 82.0% and the decision making tree analysis explained 80.5%. From the results of this study, it is believed that the sensitivity, the classification accuracy, and the logistics regression analysis as shown in a higher degree may be useful materials to establish a depression prediction model for the community residents.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.114-119
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• 2009

본 논문에서는 수량화 방법과 AHP(Analytic Hierarchy Process) 기법을 사용하여 산사태 발생에 대한 통계적 예측모형을 구축하는데 목적이 있다. 수량화(Quantification) 방법은 질적변수에 수량을 부여하는 통계적 방법으로, 기 조사된 자료에 기반하여 분석을 수행하는 방법이다. 본 논문에서는 서구의 다변량분석 기법인 정준상관분석의 결과를 토대로 수량화 과정을 구체적으로 제안한다. 데이터에 기반한 수량화 방법과는 달리 AHP(Analytic Hierarchy Process) 기법은 일종의 다기준 의사결정을 위해 사용되는 기법으로, 설문자료에 기반한 분석법이다. 실제자료에 대한 분석으로 산사태 발생여부를 측정한 자료(한국지질자원연구원 제공)와 전문가 설문을 통해 수집된 자료를 이용하였다. 이들 자료에 대해 수량화 분석과 AHP분석을 통해 산사태 발생여부를 예측할 수 있는 두 종류의 평가표와 함께 로지스틱 회귀를 통한 통계적 예측모형을 개발하였으며, 두 모형간의 성능비교와 안정성 평가를 수행하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.192-192
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• 2020

취약성 분석의 결과로 폭설에 의한 기후노출은 현재에는 강원권이 가장 취약한 것으로 나타났다. 그러나 미래에는 강원권, 충청권, 호남권을 연결하는 축으로 취약지역이 확대될 것으로 전망된다. 본 연구에서는 다양한 머신러닝 기법을 이용하여 대설피해 예측을 실시하였다. 머신러닝 기법으로는 로지스틱회귀모형, 서포트벡터 머신, 의사결정트리 모형을 적용하였다. 종속변수로 대설피해액 자료를 이용하였고, 독립변수로 기상관측자료, 사회·경제적 요소를 사용하였다. 결과적으로 기존에 사용했던 다중회귀모형과 머신러닝 기법으로 예측한 예측력을 비교 및 분석하였고, 예측력이 가장 높은 머신러닝 기법을 제시하였다. 본 연구에서 대설피해 예측을 위해 사용된 예측력이 가장 높은 기법을 활용하여 대설피해를 예측한다면, 미래에 전국적으로 확대될 대설피해에 대해 효과적으로 대비할 수 있을 것으로 기대된다.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.531-539
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• 2009

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.