• Journal of the Korean Statistical Society
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• v.18 no.1
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• pp.46-61
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• 1989

This paper is concerned with the development of a nonparametric procedure for the statistical inference about the nonlinear regression parameters. A confidence region and a hypothesis testing procedure based on a class of signed linear rank statistics are proposed and the asymptotic distributions of the test statistic both under the null hypothesis and under a sequence of local alternatives are investigated. Some desirable asymptotic properties including the asymptotic relative efficiency are discussed for various score functions.

• Journal of the Korean Statistical Society
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• v.33 no.1
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• pp.59-78
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• 2004

A regression function in generalized linear model may have a discontinuity/change point at unknown location. In order to estimate the location of the discontinuity point and its jump size, the strategy is to use a nonparametric approach based on one-sided kernel weighted local-likelihood functions. Weak convergences of the proposed estimators are established. The finite-sample performances of the proposed estimators with practical aspects are illustrated by simulated examples.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.2
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• pp.178-195
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• 2019

In this study, we examine business start-up and closing business in a geographical context. In particular, we analyze the geographical characteristics of business start-up and closing business according to the type of industry. For the purpose, we use the last 10 years data that have been related with current economic situation since the financial crisis. In first, we identify the spatial distribution patterns of business start-up and closing business, We examine the difference between individual businesses and corporations. Finally, we construct general linear regression models and spatial regression models for them, and derive meaningful socioeconomic variables that explain their location distribution. The results of this study could provide basic data for regional planning of national and local governments that activate local economies as well as job creation.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1223-1232
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• 2011

Tong and Wang's estimator (2005) is a new approach to estimate the error variance using least squares method such that a simple linear regression is asymptotically derived from Rice's lag- estimator (1984). Their estimator highly depends on the setting of a regressor and weights in small sample sizes. In this article, we propose a new approach via a local quadratic approximation to set regressors in a small sample case. We estimate the error variance as the intercept using a ridge regression because the regressors have the problem of multicollinearity. From the small simulation study, the performance of our approach with some existing methods is better in small sample cases and comparable in large cases. More research is required on unequally spaced points.

• Journal of the Korean Statistical Society
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• v.20 no.1
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• pp.85-92
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• 1991

A set of conditions ensuring local asymptotic normality for independent but not necessarily identically distributed observations in semiparametric models is presented here. The conditions are turned out to be more direct and easier to verify than those of Oosterhoff and van Zwet(1979) in semiparametric models. Examples considered include the simple linear regression model and Cox's proportional hazards model without censoring where the covariates are not random.

• The Journal of Information Systems
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• v.29 no.3
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• pp.35-52
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• 2020

Purpose Local governments in each region actively hold local festivals for the purpose of promoting the region and revitalizing the local economy. Existing studies related to local festivals have been actively conducted in tourism and related academic fields. Empirical studies to understand the effects of latent variables on local festivals and studies to analyze the regional economic impacts of festivals occupy a large proportion. Despite of practical need, since few researches have been conducted to predict the number of visitors, one of the criteria for evaluating the performance of local festivals, this study developed a model for predicting the number of visitors through various observed variables using a machine learning algorithm and derived its implications. Design/methodology/approach For a total of 593 festivals held in 2018, 6 variables related to the region considering population size, administrative division, and accessibility, and 15 variables related to the festival such as the degree of publicity and word of mouth, invitation singer, weather and budget were set for the training data in machine learning algorithm. Since the number of visitors is a continuous numerical data, random forest, Adaboost, and linear regression that can perform regression analysis among the machine learning algorithms were used. Findings This study confirmed that a prediction of the number of visitors to local festivals is possible using a machine learning algorithm, and the possibility of using machine learning in research in the tourism and related academic fields, including the study of local festivals, was captured. From a practical point of view, the model developed in this study is used to predict the number of visitors to the festival to be held in the future, so that the festival can be evaluated in advance and the demand for related facilities, etc. can be utilized. In addition, the RReliefF rank result can be used. Considering this, it will be possible to improve the existing local festivals or refer to the planning of a new festival.

• Journal of the Korean Society of Food Culture
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• v.30 no.1
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• pp.54-63
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• 2015

This study was conducted to examine the effects of local food value perception on purchasing and experience in consumers. ${\chi}^2$-test, ANOVA, and linear regression analysis were conducted. The findings are summarized as follows: The most common place for buying agricultural products was 'hypermarkets' (41.7%), and the most important factor for purchasing local food was 'local government's certification products' (23.7%). The most important value recognition item for local food was 'I think that local food is a high-quality agricultural products'. (3.74 points), followed by 'I think that local food have a value of respect for customers' (3.61 points) and 'I have a faith for the local food'. (3.61 points) in that order. The main tourism experience activity was 'food experience' (49.0%), and information source of local food experience tourism was 'mass media (TV, newspapers, etc.)' (37.3%). As age increased, experience of local food also increased. The most effectual value recognition item for purchasing local food was 'I think that local food have a value of respect for customers'. The most effectual value recognition item for increasing intake experience of local food was 'I think that the local food is high-quality agricultural products'.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.1
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• pp.14-28
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• 2007

This study presented the selection of ozone ($O_3$) potential factors and designed and assessed its potential prediction model using multiple-linear regression equations in Ulsan area during the springtime from April to June, $2000{\sim}2004$. $O_3$ potential factors were selected by analyzing the relationship between meterological parameters and surface $O_3$ concentrations. In addition, cluster analysis (e.g., average linkage and K-means clustering techniques) was performed to identify three major synoptic patterns (e.g., $P1{\sim}P3$) for an $O_3$ potential prediction model. P1 is characterized by a presence of a low-pressure system over northeastern Korea, the Ulsan was influenced by the northwesterly synoptic flow leading to a retarded sea breeze development. P2 is characterized by a weakening high-pressure system over Korea, and P3 is clearly associated with a migratory anticyclone. The stepwise linear regression was performed to develop models for prediction of the highest 1-h $O_3$ occurring in the Ulsan. The results of the models were rather satisfactory, and the high $O_3$ simulation accuracy for $P1{\sim}P3$ synoptic patterns was found to be 79, 85, and 95%, respectively ($2000{\sim}2004$). The $O_3$ potential prediction model for $P1{\sim}P3$ using the predicted meteorological data in 2005 showed good high $O_3$ prediction performance with 78, 75, and 70%, respectively. Therefore the regression models can be a useful tool for forecasting of local $O_3$ concentration.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.471-473
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• 2010

본 논문은 선형회귀(LR: Linear Regression)와 국부적인 방사기저함수 네트워크(RBFN: Radial Basis Function Networks)를 결합한 점진적인 모델(incremental model)의 설계와 관련되어진다. 전형적인 RBFN에 의한 모델링과는 달리, 제안된 방법의 근본적인 원리는 두 단계에 의해 고려되어진다. 첫째, 전체 모델의 설계과정에서 전역적인 모델로써 선형회귀에 의해 데이터의 선형부분을 구축한다. 다음으로, 모델링 오차는 오차가 존재하는 국부적인 공간에서 RBFN에 의해 보상되어진다. 여기서, 오차의 분포로부터 RBFN을 설계하기 위해 컨텍스트 기반 퍼지 클러스터링(CFC: Context-based Fuzzy Clustering)를 통해 정보입자의 형태로 구축되어진다. 실험은 자동차 mpg 연료소비량 예측과 부동산 가격예측문제를 통해 제안된 방법의 우수성을 증명한다.