• Korean Journal of Mathematics
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• 제22권2호
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• pp.289-305
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• 2014

In this paper we propose a new approach to the variable selection problem for a primary resolution and wavelet basis functions in wavelet regression. Most wavelet shrinkage methods focus on thresholding the wavelet coefficients, given a primary resolution which is usually determined by the sample size. However, both a primary resolution and the basis functions are affected by the shape of an unknown function rather than the sample size. Unlike existing methods, our method does not depend on the sample size and also takes into account the shape of the unknown function.

• 응용통계연구
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• 제31권4호
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• pp.447-461
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• 2018

국내 영화 산업 매출은 매년 증가하고 있다. 극장은 영화의 1차 판매 경로이며, 극장을 이용하는 관객 수는 부가판권에 영향을 준다. 따라서 극장을 이용하는 관객의 수는 영화 산업 매출에 직결되는 중요한 요소이다. 본 논문에서 특정일의 관객 수를 예측하기 위하여 다중선형회귀모형과 Bass 모형을 결합한 Hybrid 모형을 고려한다. 두 모형을 결합함으로써 회귀분석의 예측값을 Bass 모형의 예측값으로 보정하였다. 분석에는 개봉일이 모두 다른 세 영화를 이용하였다. All subset regression 방법을 이용해 모든 가능한 조합을 생성하고 5중 교차검증(5-fold cross validation)을 통해 5번 모형을 추정한다. 이 때 제곱근평균오차가 가장 작은 모형으로 예측값을 구한 뒤 Bass 모형의 예측값과 결합해 최종 예측값을 구하게 된다. 과거데이터가 존재할수록 Bass 모형의 가중치는 증가하면서 예측값에 보정효과를 준다는 것을 확인할 수 있었다.

• 응용통계연구
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• 제34권4호
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• pp.589-597
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• 2021

단순회귀와 다중회귀에서 회귀계수의 의미는 차이가 있고 회귀계수의 추정값은 같지 않을 뿐 아니라 그 부호가 서로 다른 경우도 발생한다. 회귀모형에서 설명변수의 상대적 기여도의 파악은 회귀분석의 수행의 중요한 부분이다. 표준화 회귀모형에서 표준화 회귀계수는 해당 설명변수를 제외한 나머지 설명변수의 값이 고정되어있는 상황에서 설명변수가 표준편차만큼 증가하였을 때 반응변수가 표준편차를 기준으로 얼마나 변화했는가로 해석할 수 있지만 표준화 회귀계수의 크기가 각 설명변수의 상대적 중요도를 나타내는 척도라고 할 수 없음은 잘 알려져 있다. 본 논문에서는 다중회귀에서 회귀계수의 추정량을 상관계수와 결정계수의 함수로 나타내고 이를 추가적인 설명력과 추가적인 결정계수의 관점에서 생각해 본다. 또한 다양한 산점도에서의 상관계수와 회귀계수 추정값의 관계를 알아보고 설명변수가 두 개인 경우에 구체적으로 적용해 본다.

• 한국도로학회논문집
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• 제15권3호
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• 경영과정보연구
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• 제15권
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• pp.147-164
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• 2004

The purpose of this paper is on employees as internal customers and the critical role this group plays in the delivery of quality results. The set up of research model for verification was as follows. The research model was drawn as internal service quality level $\Rightarrow$ internal customer satisfaction $\Rightarrow$ enterprise outcome. Then, two hypotheses were established to the research model. Through the factor analysis and multiple regression analysis, the results are as follows. First, internal service quality level turned out to be affected indirectly through internal customers' satisfaction rather than a direct factor to affect the enterprise outcome. Second, internal customers' satisfaction was proved to be the most important factor for the enterprise outcome as ti was the intimate factor precedent to the enterprise outcome. However, there could be a variation of response according to the personal circumstances of respondents since the respondents were from different enterprises and consisted various job positions and age group. Namely it included a limitation of rather unaccurate resulting values because the transverse methods were performed for convenience though it needed a longitudinal research to accomplish the general purpose of this study.

• 지질공학
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• 제18권1호
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• pp.27-36
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• 2008

이 연구는 토질특성을 평가하는 주요 영향인자인 투수계수를 몇 가지 토질물성만으로 쉽게 산정할 수 있도록 단순회귀 분석을 이용하여 투수계수산정모델을 개발하였다. 이를 위한 연구지역은 강원도 평창군 진부면지역으로 총 45개 지점에서 토층시료를 채취하여 실내에서 여러 토질시험을 실시하였다. 상관분석을 통해 시험결과들 중 투수계수에 유효한 토질인자를 선별한 후 선별된 인자들과 직접투수시험에 의한 투수계수간의 관계를 단순회귀분석으로 공식화한 투수계수산정모델을 개발하였다. 또한, 개발된 투수계수산정모델과 직접투수시험 및 각 경험식들에 의한 투수계수를 비교 분석하여 모델의 적합성을 검증하였다. SPSS(statistical package for the social sciences)를 이용하여 여러 토질물성과 투수계수간의 상관관계를 분석한 결과 유효경, 간극비, 건조단위중량이 투수계수에 가장 크게 영향을 미치는 토질인자인 것으로 나타났다. 투수계수 산정모델에 의한 투수계수는 직접투수시험에 의한 투수계수와 거의 유사한 결과를 보였다. 따라서 개발된 투수계수산정모델은 연구지역과 같은 토질조건인 경우 토층의 투수계수 산정을 위한 모델로 이용이 가능할 것으로 사료된다.

• 한국환경보건학회지
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• 제48권4호
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• pp.236-243
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• 2022

Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body. Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter. Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers. Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model. Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals.

• 물과 미래
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• 제17권2호
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• pp.107-112
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• 1984

저수지내의 퇴사량을 추정하기 위하여 국내 3개 유역의 66개 관개용 저수지의 퇴사실측자료를 사용하여 저수지 퇴사량과 유역면적, 토사포착효율, 유역의 경사, 유역의 형상계수 및 저수지퇴사기간간의 상관관계를 단순회귀모형과 다도수회귀모형으로 제안하였다. 제안된 모형의 적합성을 실측자료로부터 검정하였으며 그 결과 다도수회귀모형에 의한 것이 단순회귀모형에 의하여 산정된 것보다 훨씬 정확한 것으로 판정되었다. 저수지의 년비유사량과 유역면적 및 토사포착효율과 상관시켰다. 저수지내의 년평균퇴사율과 년평균저수지내용적용적의 변동은 토사포착율에 의해 크게 좌우됨을 알았다.

• 한국디지털건축인테리어학회논문집
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• 제12권1호
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• pp.71-79
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• 2012

This study aims at finding relationships between the competitiveness of cities and the size or distance of them, based on some premises; (1) the competitiveness can be measured on the interval-ratio level, that is, factor scores, (2) a hypothesis that the spatial interaction model is valid for the relationships can be generally accepted. Based on the general recognition a research hypothesis that the more is the population or the nearer is the distance from a central city the higher is the competitiveness score is constructed. According to the premises 5-factor scores and composite score are calculated by means of regression method, and the scores are regressed on cities' populations and distances from Seoul city. Using bootstrapping method for the tests of significance is effective due to small sample of 21 cities. Results of the analyses show that most aspects of the hypothesis should be rejected or adjusted. Scores on Health-welfare factor, public service factor, and commercial vitality factor have no relation to the cities' sizes or distances. But the results also find the facts that the strong (negative) relationships exist between (1) educational base factor score and population, (2) density factor score and distance. Although this study improves systematic and analytic understanding of spatial interaction patterns, the understanding should be invalid for the general context because it has used the data on 21 cities in the capital region at the time of 2009.

• Computers and Concrete
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• 제19권5호
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.