• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.381-393
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• 2005

As a promising technique for dimension reduction in regression analysis, Sliced Inverse Regression (SIR) and an associated chi-square test for dimensionality were introduced by Li (1991). However, Li's test needs assumption of Normality for predictors and found to be heavily dependent on the number of slices. We will provide a unified asymptotic test for determining the dimensionality of the SIR model which is based on the probabilistic principal component analysis and free of normality assumption on predictors. Illustrative results with simulated and real examples will also be provided.

• Korean Journal of Social Welfare
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• v.61 no.2
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• pp.85-106
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• 2009

The main purpose of this paper is to examine the structural determinants of regional variations of poverty in Korea. Poverty rates and independent variables in Seoul, 6 other metropolitan areas, and 8 provinces except Jejudo from the year of 1998 through 2006 were pooled as unit of analysis. The pooled cross-sectional time-series regression(TSCSREG) using SAS program was adapted for the analysis. As a result of the analysis, absolute poverty and relative poverty of Gangwondo and Chungcheongnamdo were relatively higher, and that of Seoul and Ulsan metropolitan area were lower than other areas. And, the increase of financial self-reliance, social welfare expenditure, rate of standard workers, and rate of workers in manufacturing sector were associated with lower poverty rates. Therefore, place-based policies should be considered as another poverty-fighting tool in conjunction with people-based policies.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.271-280
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• 2017

Real-estate values and related economics are often the first read newspaper category. We are concerned about the opinions of experts on the forecast for real estate prices. The Box-Jenkins ARIMA model is a commonly used statistical method to predict housing prices. In this article, we tried to predict housing prices by combining independent component analysis (ICA) in multivariate data analysis and the Box-Jenkins ARIMA model. The two independent components for both the selling price index and the long-term rental price index were extracted and used to predict the future values of both indices. In conclusion, it has been shown that the actual indices and the forecast indices using ICA are more comparable to the forecasts of the ARIMA model alone.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.91-101
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• 2001

It has been shown that the multiple trees predictors are more accurate in reducing test set error than a single tree predictor. There are two ways of generating multiple trees. One is to generate modified training sets by resampling the original training set, and then construct trees. It is known that arcing algorithm is efficient. The other is to perturb randomly the working split at each node from a list of best splits, which is expected to generate reasonably good trees for the original training set. We propose a new combined multiple regression trees predictor which uses the latter multiple regression tree predictor as a predictor based on a modified training set at each stage of arcing. The efficiency of those prediction methods are compared by applying to high throughput screening of chemical compounds for biological effects.

• The Korean Journal of Financial Management
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• v.14 no.3
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• pp.157-181
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• 1997

본 연구에서는 우리나라 증시에서 베타 이외에 규모와 장부가/시장가 비율이 수익률의 차이를 설명하는데 유용한가를 실증하고 이를 토대로 13개 산업에 대하여 자본비용을 추정하였다. 1980년에서 1995년 사이의 기간에 대하여 실증한 결과, 우리나라 증시에서 규모요인은 시장위험이 설명하지 못하는 수익률 차이 부분을 상당히 설명하고 있었으나 장부가/시장가 비율은 추가적인 설명력을 보이지 못하였다. 산업별 자본비용을 추정한 결과는 만족스럽지 못하였다. 우선 해당기간 동안에 시장프레미엄이 연 1.33%로 지나치게 낮게 측정되었으며 표준오차는 연 5.85%로 매우 크게 나왔다. 회귀계수의 표준오차를 줄이기 위하여 rolling 회귀분석의 방법을 사용하였지만, 3요인 모형에서 시장위험계수(베타)에 대한 표준오차만 줄어들었을 뿐 나머지 계수들의 표준오차는 오히려 증가하였다. 회귀계수가 지니는 불확실성이 위험프레미엄의 불확실성과 결합되어서 산업별 자본비용에 대한 추정은 매우 부정확하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.33-40
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• 2011

This paper presents the prediction and evaluation of landslide using LRA(logistic regression analysis) and ANN (Artificial Neural Network) methods. In order to assess the landslide, we selected Sarabong, Byeoldobong area and Mt. Song-ak in Jeju Island. Five factors which affect the landslide were selected as: slope angle, elevation, porosity, dry density, permeability. So as to predict and evaluate the landslide, firstly the weight value of each factor was analyzed by LRA(logistic regression analysis) and ANN(Artificial Neural Network) methods. Then we got two prediction maps using AcrView software through GIS(Geographic Information System) method. The comparative analysis reveals that the slope angle and porosity play important roles in landslide. Prediction map generated by LRA method is more accurate than ANN method in Jeju. From the prediction map, we found that the most dangerous area is distributed around the road and path.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1103-1111
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• 2015

This paper presents a new prediction method based on relative error incorporated with a penalized regression. The proposed method consists of fully data-driven procedures that is fast, simple, and easy to implement. An example of real data analysis and some simulation results were given to prove that the proposed approach works in practice.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.45-53
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• 2003

One of decision tree method is regression trees which are used to predict a continuous response. The general splitting criteria in tree growing are based on a compromise in the impurity between the left and the right child node. By picking or the more interesting subsets and ignoring the other, the proposed new splitting criteria in this paper do not split based on a compromise of child nodes anymore. The tree structure by the new criteria might be unbalanced but plausible. It can find a interesting subset as early as possible and express it by a simple clause. As a result, it is very interpretable by sacrificing a little bit of accuracy.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.195-202
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• 2006

본 연구에서는 보다 효과적인 기업부도예측을 위하여, 동계적 방법과 인공지능 방법을 결합한 통합모형을 제시하였다. 이를 위하여 통계적인 모형 중에서 가장 널리 활용되고 있는 다변량 판별분석, 로지스틱 회귀분석과 인공 지능적인 방법으로서 최근 널리 사용되고 있는 인공신경망, 규칙유도기법, 베이지안 망의 5가지 방법론을 통합한 Voting with Performance & Weights from ANN(WP-ANN) 통합모형을 제시하였다. 실험결과, 본 연구에서 제안한 WP-ANN 통합모형은 다변량 판별분석, 로지스탁 회귀분석, 인공신경망, 규칙유도기법, 베이지안 망 등의 단일모형과 비교한 결과 가장 예측정확성이 유수한 것으로 나타났다. 따라서 본 연구를 통해 기업부도예측에 있어서 WP-ANN 통합모형이 기존의 모형들에 비해 우수한 예측정확성을 나타냄을 알 수 있었다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2015.11a
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• pp.175-185
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• 2015

본 연구에서는 TOD는 대중교통수단의 분담율 제고가 목적인데 이를 위한 정책대안의 모색에 필요한 새로운 분석기법을 제시하였다. 교통수단의 결정요인은 개인 특성과 라이프스타일 그리고 도시공간특성인데 이 요인들은 상호의존적으로 복합적 결합작용은 한다. 또한 개인과 그룹 및 지역 특성은 서로 다른 차원에서 상호의존성을 갖는다. 전통적 회귀분석과는 달리 위계적 분석모형은 요인들 간의 상호의존성과 복합결합적 영향관계를 규명하는 장점이 있다. 이러한 분석 기법에 의한 결과는 보다 효율적인 TOD정책 대한의 모색에 많은 기여를 할 것으로 기대된다.