• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.305-317
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• 2006

Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

• Journal of Korea Technology Innovation Society
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• v.12 no.3
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• pp.614-637
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• 2009

To enhance the efficiency of national R&D programs, it is the most important thing to establish the optimized evaluation system. This is because the distorted evaluation could be an obstacle to select the best suited research team and lead the desirable research result. In current evaluation process of national R&D program, it is the most frequently used method for eliminating an evaluator's biased view to exclude both the maximum and minimum scores. However, there are several limitations in this stereotype method. Therefore, this study analyzes some real cases to derive the problems from the stereotype method and then recommends the complementary alternatives which can enhance the reliability of the evaluation system for national R&D programs. The results acquired from this study seem to be helpful to increase the agreement of the applicants on the evaluation results and ultimately to maximize the efficiency of national R&D program. In addition, they can be applied to various field with respect to the methodology for ranking decision.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.899-904
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• 2022

As the world becomes an aging society due to a decrease in the birth rate and an increase in life expectancy, a system for health management of the elderly population is needed. Among them, various studies on occupancy and activity types are being conducted for smart home care services for indoor health management. In this paper, we propose a random forest model that classifies activity type as well as occupancy status through indoor temperature and humidity, CO2, fine dust values and UWB radar positioning for smart home care service. The experiment measures indoor environment and occupant positioning data at 2-second intervals using three sensors that measure indoor temperature and humidity, CO2, and fine dust and two UWB radars. The measured data is divided into 80% training set data and 20% test set data after correcting outliers and missing values, and the random forest model is applied to evaluate the list of important variables, accuracy, sensitivity, and specificity.