• Journal of Korean Society of Coastal and Ocean Engineers
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• v.23 no.6
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• pp.496-501
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• 2011

The missing data patterns of tidal elevation data in Korean coasts are analysed and provided. The missing interval of the data is displayed for all stations using the missing data indicator matrix in order to identify the overall missing pattern. The spatial and temporal missing rates are also estimated. The total missing rate of tidal elevation data is low. However, most of the missing is mainly derived from just 1 or 2 specific stations. The autocorrelation function of the consecutive missing interval data also shows that the missing interval occurs randomly.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.276-284
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• 2012

Classification algorithms generally assume that the data is complete. However, missing values are common in real data sets due to various reasons. In this paper, we propose to use locally linear reconstruction (LLR) for missing value imputation to improve the classification performance when missing values exist. We first investigate how much missing values degenerate the classification performance with regard to various missing ratios. Then, we compare the proposed missing value imputation (LLR) with three well-known single imputation methods over three different classifiers using eight data sets. The experimental results showed that (1) any imputation methods, although some of them are very simple, helped to improve the classification accuracy; (2) among the imputation methods, the proposed LLR imputation was the most effective over all missing ratios, and (3) when the missing ratio is relatively high, LLR was outstanding and its classification accuracy was as high as the classification accuracy derived from the compete data set.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.21 no.2
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• pp.275-285
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• 1991

The clinical and radiographic features of 655 congenitally missing teeth were studied with full mouth periapical radiograms and/or pantomograms from 368 persons visited the Department of Oral Radiology, Infirmary of Dentistry, Kyung Hee University during January 1981 to December 1989. The obtained results were as follows: 1. The prevalence of congenitally missing teeth was revealed to be 8.75% in total examined persons, and there was a higher prevalence in females (9.5%) than in males (8.0%). 2. The most frequently missing teeth were mandibular second premolars (24.6%), followed by mandibular lateral incisors (21.7%), maxillary second premolars (16.2%), and maxillary lateral incisors (11.5%). 3. There was a higher prevalence in the mandible (60.3%) than in the maxilla (39.7%), and no significant differences between right (49.65%) and left (50.35%) side. 4. In number of congenitally missing teeth per person, 54.6% had one missing tooth, and 32.9% had two missing teeth. 5. In persons with one or two congenitally missing teeth, the most frequently missing tooth was mandibular lateral incisor, and the second premolar was the tooth most frequently missing in those persons with more than three congenitally missing teeth.

• Korean Journal of Social Welfare
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• v.61 no.2
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• pp.371-389
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• 2009

This study estimates the socioeconomic costs of missing children in Korea. The costs were classified as direct costs and indirect costs. The direct costs consisted of direct costs for searching for missing child such as making posters, transportation, and medical costs. The indirect costs were computed by the opportunity costs caused by child missing. The total costs that could be attributable to missing child were estimated to be about 570 million won per long-term missing child. This provides strong evidence that prevention of child missing is the most important and quick recovery after child is missing is the second most important. Missing child incurs substantial socioeconomic costs to the Korean society. Therefore, this study provides strong need for more interest from people who are indifferent to missing child issues and strong support for more government interventions to solve missing child problem in Korea. Further studies are needed to calculate socioeconomic costs of child missing more exactly.

• Honam Mathematical Journal
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• v.4 no.1
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• pp.147-154
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• 1982

Estimation methods of missing points for an experimental design are described. Formulae are provided for the estimation of missing points using matrix notation. The correct analysis of variance table is given. Estimation methods of a single missing point and two missing points in $2^{n}$ factorial designs are described.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.293-302
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• 2019

In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.257-268
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• 2022

When there is a missing value in the raw data, if ignore the missing values and proceed with the analysis, the accuracy decrease due to the decrease in the number of sample. The method of imputation and analyzing patterns and significant values can compensate for the problem of lower analysis quality and analysis accuracy as a result of bias rather than simply removing missing values. In this study, we proposed to study irregular data patterns and missing processing methods of data using machine learning techniques for the study of correction of missing values. we would like to propose a plan to replace the missing with data from a similar past point in time by finding the situation at the time when the missing data occurred. Unlike previous studies, data correction techniques present new algorithms using DNN and KNN-MLE techniques. As a result of the performance evaluation, the ANAE measurement value compared to the existing missing section correction algorithm confirmed a performance improvement of about 0.041 to 0.321.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.579-590
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• 2003

When we analyze incomplete data, i.e., data with missing values, we need treatment for the missing values. A common way to deal with this problem is to delete the cases with missing values. Various other methods have been developed. Among them are EM algorithm and regression algorithm which can estimate missing values and impute the missing elements with the estimated values. In this paper, we introduce multiple imputation software SOLAS which generates multiple data sets and imputes with them.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.167-174
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• 2002

In the case of "nonignorable missing data", it is necessary to assume a model dealing with the missing on each situations. In this article, for example, we sometimes meet situations where data set are income amounts in a survey of individuals and assume a model as the values are the larger, a missing data probability is the higher. The method is to maximize using the EM(Expectation and Maximization) algorithm based on the (missing data) mechanism that creates missing data of the case of exponential distribution. The method started from any initial values, and converged in a few iterations. We changed the missing data probability and the artificial data size to show the estimated accuracy. Then we discuss the properties of estimates.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.133-152
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• 1998

The central composite design is widely used in the response surface analysis, because it can fit the second order model with small experimental points. In practice, the experimental data are not always obtained on all the points. When there are missing observations, many problems due to the missing cells can occur. In this paper, the influence of a missing cell on the central composite design is discussed. First, the influences of a missing cell on the variances of estimated regression coefficents are compared as $\alpha$ varies. Second, how the average predition variance is affected by a missing sell is discussed. And the influence on rotatability is investigated. Third, the influence of a missing cell on optimality, especially on D-optimality and A-optimality, is examined.