• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.335-346
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• 2022

Profile control chart aims to detect a change in the functional relationship of multivariate characteristics in the statistical process control. In monitoring two variables, a linear profile is of interest composed of the intercept and slope of one variable (response variable) against the other (explanatory variable). The previous studies on monitoring of the linear profile mostly assume that the explanatory variables are the same for all profiles. However, there are also cases where they vary depending on profiles. This paper intends to extend the monitoring method to where explanatory variables are different for each profile. We compare the new method's performance through simulation and apply it to monitoring a network intrusion using NSL-KDD data.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1361-1371
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• 2016

Quantile regression provides a variety of useful statistical information to examine how covariates influence the conditional quantile functions of a response variable. However, traditional quantile regression (which assume a linear model) is not appropriate when the relationship between the response and the covariates is a nonlinear. It is also necessary to conduct variable selection for high dimensional data or strongly correlated covariates. In this paper, we propose a penalized quantile regression tree model. The split rule of the proposed method is based on residual analysis, which has a negligible bias to select a split variable and reasonable computational cost. A simulation study and real data analysis are presented to demonstrate the satisfactory performance and usefulness of the proposed method.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.366-370
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• 2013

Regression Analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper introduce Theil's method to find a fuzzy regression model which explain the relationship between explanatory variable and response variables. Theil's method is a robust method which is not sensive to outliers. Theil's method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. We propose an example to show Theil's estimator is robust than the Least squares estimator.

• Journal of Korean Library and Information Science Society
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• v.42 no.4
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• pp.243-262
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• 2011

The aim of this study was to investigate musculo-skeletal symtoms and working conditions of university library's librarians to search for the risk factors related to musculo-skeletal symptoms. The study subjects were 266 librarians who were working at 20 university libraries. A self-recording questionnaire was used to investigate the general characteristics, working conditions, job intensity, job satisfaction and stress, education of musculoskeletal disorders and nature of musculoskeletal symptom. Statistical analysis was done by using t-test and multiple regression analysis. The complaint proportion of self-reported positive musculoskeletal symptoms was 62.5% and that of severe musculoskeletal symptoms was 26.1%. Multiple regression analysis showed that low satisfaction of working conditions, high job intensity, irregular mealtime, job stress were closely related to the positive rate of musculoskeletal symptoms. Therefore, it will be necessary to make efforts to reduce the prevalence of musculoskeletal disorders improving working conditions and mitigating the job intensity.

• 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.

• Proceedings of the KOR-KST Conference
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• 1999.03a
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• pp.95-109
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• 1999

기존의 추정된 화물 수요모형은 화물의 출하특성과 관련된 설명변수를 중심으로 추정되었으며, 이에 따라 수송수단 선택 과정에서 화주가 느끼는 실제의 인식 상황을 모형내에 적절히 반영하지 못하였다. 본 연구는 기존 연구가 갖는 한계점을 극복하고자 화주가 수송수단을 선택할 때 느끼는 인식상황을 모형 내에 적용시켜 수단 선택 특성을 분석하였다. 연구대상은 우리나라의 188개 제조업체에서 화물자동차로 출하한 내수용 화물이며, 연구의 범위도 현실 운송체계 내에서 화주의 수단선택 행태를 설명하는 단기간의 예측으로 제한하였다. 모형추정결과 우리나라의 공로화물수송을 해석하기 위해서는, 출하중량까지를 고려한 다항로짓모형 형태이면서 인식 요소를 행태변수로 추가한 모형을 이용하는 것이 가장 적절하다는 결론을 내렸다. 그리고 이에 따라 주요한 설명 변수들의 탄력성과 화주의 인식 요소에 대한 특성값을 분석하여 제시하였다. 연구결과는 활용성 측면에서 직접 활용이 가능한 것과 잠재적인 변화를 예측하는데 이용되는 것으로 구분된다. 먼저 직접활용이 가능한 것은 수송수단과 관계된 변수들을 해석하여 얻는데, 수송비용과 수송시간에 대한 계수값의 크기와 부호, 그리고 탄력성은 정부의 정책부서나 운송인의 계획수립에 직접 적용된다. 다음으로 화주의 인식 요소는 잠재적인 변화를 예측하는데 이용되며 각 요소가 갖는 탄력성 및 특징은 운송인의 고객관리 기준이된다.

• Journal of Korean Society of Transportation
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• v.16 no.4
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• pp.187-194
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• 1998

본 연구는 교통사고 심각도와 관련된 중요변수를 찾고 이들 변수를 바탕으로 신경망, Decision Tree, 로지스틱 회귀분석을 이용하여 사고 심각도 분류 예측모형을 추정하였다. 다수의 범주형 변수로 이루어진 교통사고 통계원표상의 설명변수 들로부터 사고 심각도 변화에 영향력 있는 변수 선택을 위하여 독립성 검정을 위한 $x^2$ test와 Decision Tree를 이용하였고, 선택된 변수들은 신경망과 로지스틱 회귀분석의 기초로 이용되었다. 분석결과 세가지기법간에 분류정확도에는 유의한 차이가 없는 것으로 나타났다. 그러나 Decision Tree가 설명변수 선택능력과 분석수행시간, 사고 심각도 결정요인 식별의 용이함 측면에서 범주형 종속변수인 사고 심각도의 분석에 적합한 것으로 보이며 사고 심각도에는 보호장구가 가장 큰 영향을 미치는 것으로 재입증되었다.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.373-381
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• 1998

본 연구는 교통사고 심각도와 관련된 중요변수를 찾고 이들 변수를 바탕으로 신경망, Decision Tree, 로지스틱 회귀분석을 이용하여 사고 심각도 분류 예측모형을 추정하였다. 다수의 범주형 변수로 이루어진 교통사고 통계원표상의 설명변수 들로부터 사고 심각도변화에 영향력 있는 변수선택을 위하여 $X^2$ 독립성 검정과 Decision Tree를 이용하였고, 선택된 변수들은 신경망과 로지스틱 회귀분석의 기초로 이용되었다. 분석결과 세가지기법간에 분류정확도에는 유의한 차이가 없는 것으로 나타났다. 그러나 decision Tree가 설명변수 선택능력과 분석수행시간, 사고 심각도 결정요인 식별의 용이함 측면에서 범주형 종속변수인 사고 심각도의 분석에 적합합 것으로 보이며 사고 심각도에는 보호장구가 가장 큰 영향을 미치는 것으로 재입증되었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.3900-3914
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• 2012

This study examined the background of the recent global financial crisis and the concept of one of the financial derivatives such as the credit default swap(CDS) or synthetic CDO(collateral debt obligations), given the rapid growing and changing the over-the-counter derivative markets in their volume and structures. In comparison with the previous literature such as the study of Park & Kim (2011), this research empirically performed more thorough and comprehensive investigations to find any financial characteristics or attributes to determine the CDS spreads. Regarding the results obtained from the multiple regression models, the explanatory variables such as STYIELD3, SLOPE, INASSETS, and VOLATILITY, showed their statistically significant effects on all the tested dependent variables(DVs). Another procedure such as the principle component analysis(PCA), was also performed to account for additional IDVs as possible determinants of the dependent variables. Subsequent to this analysis, larger coefficients of each corresponding eigenvector such as BETA, PFT2, GROWTH, STD, and BLEVERAGE were found to be possible financial determinants. For robustness, all the IDVs were employed to be tested in the 'full' regression model with stepwise procedure. As a result, STYIELD3, SLOPE, and VOLATILITY, and BETA showed their statistically significant relationship with all the dependent variables of the CDS spreads.