• Title, Summary, Keyword: Missing

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Missing Pattern of the Tidal Elevation Data in Korean Coasts (한반도 연안 조위자료의 결측 양상)

  • Cho, Hong-Yeon;Ko, Dong-Hui;Jeong, Shin-Taek
    • 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.

Missing Value Imputation based on Locally Linear Reconstruction for Improving Classification Performance (분류 성능 향상을 위한 지역적 선형 재구축 기반 결측치 대치)

  • Kang, Pilsung
    • 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.

A CLINICAL AND RADIOGRAPHIC STUDY OF CONGENITALLY MISSING TEETH (선천성 결손치에 관한 임상 및 방사선학적 연구)

  • Lee Ji Min;Lee Sang Rae
    • The Journal of Korean Academy of 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.

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MissingFound: An Assistant System for Finding Missing Companions via Mobile Crowdsourcing

  • Liu, Weiqing;Li, Jing;Zhou, Zhiqiang;He, Jiling
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.10
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    • pp.4766-4786
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    • 2016
  • Looking for missing companions who are out of touch in public places might suffer a long and painful process. With the help of mobile crowdsourcing, the missing person's location may be reported in a short time. In this paper, we propose MissingFound, an assistant system that applies mobile crowdsourcing for finding missing companions. Discovering valuable users who have chances to see the missing person is the most important task of MissingFound but also a big challenge with the requirements of saving battery and protecting users' location privacy. A customized metric is designed to measure the probability of seeing, according to users' movement traces represented by WiFi RSSI fingerprints. Since WiFi RSSI fingerprints provide no knowledge of users' physical locations, the computation of probability is too complex for practical use. By parallelizing the original sequential algorithms under MapReduce framework, the selecting process can be accomplished within a few minutes for 10 thousand users with records of several days. Experimental evaluation with 23 volunteers shows that MissingFound can select out the potential witnesses in reality and achieves a high accuracy (76.75% on average). We believe that MissingFound can help not only find missing companions, but other public services (e.g., controlling communicable diseases).

Comparison of missing data methods in clustered survival data using Bayesian adaptive B-Spline estimation

  • Yoo, Hanna;Lee, Jae Won
    • Communications for Statistical Applications and Methods
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    • v.25 no.2
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    • pp.159-172
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    • 2018
  • In many epidemiological studies, missing values in the outcome arise due to censoring. Such censoring is what makes survival analysis special and differentiated from other analytical methods. There are many methods that deal with censored data in survival analysis. However, few studies have dealt with missing covariates in survival data. Furthermore, studies dealing with missing covariates are rare when data are clustered. In this paper, we conducted a simulation study to compare results of several missing data methods when data had clustered multi-structured type with missing covariates. In this study, we modeled unknown baseline hazard and frailty with Bayesian B-Spline to obtain more smooth and accurate estimates. We also used prior information to achieve more accurate results. We assumed the missing mechanism as MAR. We compared the performance of five different missing data techniques and compared these results through simulation studies. We also presented results from a Multi-Center study of Korean IBD patients with Crohn's disease(Lee et al., Journal of the Korean Society of Coloproctology, 28, 188-194, 2012).

Analysis of Socioeconomic Costs of Child Missing (아동실종으로 인한 사회경제적 비용 분석)

  • Chung, Ick-Joong;Kim, Sung-Chun;Song, Jae-Seok
    • 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.

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Estimating Missing Points In Experiments (실험(實驗)에 있어서 결측점(缺測点) 추정(推定))

  • SIM, JUNG WOOK
    • 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.

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Developing a Method to Define Mountain Search Priority Areas Based on Behavioral Characteristics of Missing Persons

  • Yoo, Ho Jin;Lee, Jiyeong
    • 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.

Application of SOLAS to the Multiple Imputation for Missing Data

  • Moon, Sung-Ho;Kim, Hyun-Jeong;Shin, Jae-Kyoung
    • 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.

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HANDLING MISSING VALUES IN FUZZY c-MEANS

  • Miyamoto, Sadaaki;Takata, Osamu;Unayahara, Kazutaka
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.139-142
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    • 1998
  • Missing values in data for fuzzy c-menas clustering is discussed. Two basic methods of fuzzy c-means, i.e., the standard fuzzy c-means and the entropy method are considered and three options of handling missing values are proposed, among which one is to define a new distance between data with missing values, second is to alter a weight in the new distance, and the third is to fill the missing values by an appropriate numbers. Experimental Results are shown.

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