• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.149-160
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• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• KDI Journal of Economic Policy
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• v.34 no.2
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• pp.139-171
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• 2012

This paper examines the effect of private tutoring expenditures on academic performance of middle school students in South Korea, using data from "Analysis of the Level of School Education and Its Actual condition: Middle School". In the face of endogeneity of private tutoring expenditures, the paper employs an instrumental variable (IV) method and a nonparametric bounding method. Using both methods we show that the true effect of private tutoring on middle school students remains at most modest in Korea. The IV results suggest that a 10 percent increase in tutoring expenditure for Korean, English and math raises a student's test score of the subject at the largest by 1.24, 1.28, and 0.75 percent, respectively. The bounding results also fail to show evidence that an increase in tutoring expenditure leads to economically and statistically significant improvements in test score.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.481-488
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• 2016

This study is about comparing the mobile user experience of Facebook and Instagram which are most often used among the recent SNSs by the people in their 30s and under. This study analyzed the user experience level after dividing the user experience factors through the Creating Pleasurable Interfaces model, and suggested the mean analysis as well as the result of Wilcoxon rank test which is a nonparametric statistics method. As a result of study, the Display information visually factor in functional factor and the configuration of the main page in convenient factor were a statistically significant difference in the mobile user experience of Facebook and Instagram. It is expected that this study may help seeking the user experience factors to be promoted preferentially in a competitive situation through the statistical comparative evaluation of the experience of two SNS users.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.147-157
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• 1993

In the survivla analysis the problem of estimating mean residual life function (MRLF) under random censoring is very important. In this paper we propose and study a nonparametric estimator of MRLF, which is a functional form based on the estimator of the survival function due to Susarla and Van Ryzin (1980). The proposed estimator is shown to be better than some other estimators in terms of mean square errors for the exponential and Weibull cases via Monte Carlo simulation studies.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.58-63
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• 1995

To access the quality of a fit to a set of data it is always useful to conduct a posteriori analysis involving the examination of residuals, detection of influential data values, etc. Smoothing splines are a type of nonparametric regression estimators for the diagnostic problem. And leverage value, Cook's distance, and DFFITS are used for detecting influential data. Since high leverage points will always have small residuals, the new diagnostic measures including of properties of leverage and residuals are needed. In this paper, we propose FVARATIO version as diagnostic measure in nonparametric regression. Also we consider the rough bound as analogy with linear regression case.

• Journal of the Korean Statistical Society
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• v.26 no.1
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• pp.101-116
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• 1997

This is primarily an expository paper that presents several nonparametric procedures for testing exponentiality against certain monotonicity properties of the mean residual life function, tests against the trend change in such function attract a great deal of attention of late in reliability analysis. In this note, we present some of the known testing procedures regarding the behavior of mean residual life function. These tests are also compared in terms of asymptotic relative efficiency and empirical power against a few alternatives. The tests based on incomplete data are also briefly discussed.

• Environmental and Resource Economics Review
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• v.10 no.3
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• pp.323-344
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• 2001

This study analyzes the relative efficiencies of three types of non-landfill treatment of food wastes; recycling to fertilizers or animal feeds, reducing the size of food wastes, and fermentation of food wastes. Unlike previous studies our study incorporates not only usual inputs and outputs but also emissions of pollutants such as odor and noise generated by the treatment facilities into the analysis. A nonparametric method suggested by Fare et al. (1989) has been used to estimate the relative efficiencies of facilities incorporating emission of pollutants. The results show that recycling is more efficient than the other two treatment methods. It is also shown that the usual models that do not incorporate the environmental aspects of the treatment facilities derive a biased conclusion on the relative efficiencies.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1214-1220
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• 2006

In this article, it will be shown that a nonparametric and data-adaptive approach to the variance change point (VCP) detection problem is possible by formulating it as a pattern classification problem. Technical aspects of the VCP detector are discussed, which include its training strategy and selection of proper classification tool.

• Journal of Korean Society for Quality Management
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• v.34 no.1
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• pp.40-47
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• 2006

Claim history data of rather long period were collected to assess reliability and warranty cost analyses. The data were appropriately organized to be used for further statistical analyses. For each critical component, nonparametric statistical method was applied to obtain reliability plot. Hazard plots of the components in a subsystem or system level were also obtained. Competing risk model was assumed to obtain the performance of the subsystem or system level.