• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1271-1277
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• 2012

In this paper, we propose an estimation method of the parameter in an exponential distribution based on a progressive Type I interval censored sample with semi-missing observation. The maximum likelihood estimator (MLE) of the parameter in the exponential distribution cannot be obtained explicitly because the intervals are not equal in length under the progressive Type I interval censored sample with semi-missing data. To obtain the MLE of the parameter for the sampling scheme, we propose a method by which progressive Type I interval censored sample with semi-missing data is converted to the progressive Type II interval censored sample. Consequently, the estimation procedures in the progressive Type II interval censored sample can be applied and we obtain the MLE of the parameter and survival function. It will be shown that the obtained estimators have good performance in terms of the mean square error (MSE) and mean integrated square error (MISE).

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.355-368
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• 2023

This article introduces the R package ELCIC (https://cran.r-project.org/web/packages/ELCIC/index.html), which provides an empirical likelihood-based information criterion (ELCIC) for model selection that includes, but is not limited to, variable selection. The empirical likelihood is a semi-parametric approach to draw statistical inference that does not require distribution assumptions for data generation. Therefore, ELCIC is more robust and versatile in the context of model selection compared to the currently existing information criteria. This paper illustrates several applications of ELCIC, including its use in generalized linear models, generalized estimating equations (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanisms of missing at random and dropout.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1311-1327
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• 2016

We propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the 'illness-death model', which composes three states, such as 'healthy', 'diseased', and 'dead'. The state of 'diseased' can be considered as an intermediate event. Two more states are added into the illness-death model to describe missing events caused by a loss of follow-up before the end of the study. One of them is a state of 'LTF', representing a lost-to-follow-up, and the other is an unobservable state that represents the intermediate event experienced after LTF occurred. Given covariates, we employ the Cox proportional hazards model with a normal frailty and construct a full likelihood to estimate transition intensities between states in the multi-state model. Marginalization of the full likelihood is completed using the adaptive Gaussian quadrature, and the optimal solution of the regression parameters is achieved through the iterative Newton-Raphson algorithm. Simulation studies are carried out to investigate the finite-sample performance of the proposed estimation procedure in terms of the empirical coverage probability of the true regression parameter. Our proposed method is also illustrated with the dataset adapted from Helmer et al. (2001).

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.379-393
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• 2020

A terminal event such as death may censor an intermediate event such as relapse, but not vice versa in semi-competing risks data, which is often seen in medicine, public health, and epidemiology. We propose a Weibull regression model with a normal frailty to analyze semi-competing risks data when all three transition times of the illness-death model are possibly interval-censored. We construct the conditional likelihood separately depending on the types of subjects: still alive with or without the intermediate event, dead with or without the intermediate event, and dead with the intermediate event missing. Optimal parameter estimates are obtained from the iterative quasi-Newton algorithm after the marginalization of the full likelihood using the adaptive importance sampling. We illustrate the proposed method with extensive simulation studies and PAQUID (Personnes Agées Quid) data.

• The Journal of Information Systems
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• v.14 no.3
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• pp.37-47
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• 2005

Mining of association rules is to find associations among data items that appear together in some transactions or business activities. As of today, algorithms for association rule mining, as well as for other data mining tasks, are mostly applied to relational databases. As XML being adopted as the universal format for data storage and exchange, mining associations from XML data becomes an area of attention for researchers and developers. The challenge is that the semi-structured data format in XML is not directly suitable for traditional data mining algorithms and tools. In this paper we present an encoding method to encode XML tree-nodes. This method is used to store the XML data in Value Table and Transaction Table that can be easily accessed via indexing. The hierarchical relationship in the original XML tree structure is embedded in the encoding. We applied this method to association rules mining of XML data that may have missing data.

• Nutritional Sciences
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• v.6 no.3
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• pp.173-184
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• 2003

Objective : This study was carried out to develop a semi-quantitative food frequency Questionnaire (SQFFQ) for estimating average dietary intake to determine the risk factor for lifestyle-related diseases in a conjoint cohort study. Design : We developed an SQFFQ for genomic epidemiological studies based on the data in the'98 Korea Health and Nutrition Examination Survey. A subset of data on informative food items was collected using the 24-hr recall method with 2,714 adults aged 40 or older living in middle-sized cities or in rural areas in Korea. The cumulative percent contribution and cumulative multiple regression coefficients of 17 nutrients (energy, fat, carbohydrate, protein, fiber, iron, potassium, sodium, calcium, phosphorus, vitamin A, retinol, $\beta$-carotene, vitamin $B_1$, vitamin $B_2$, niacin and vitamin C) of each food were computed. Results : Two hundred and forty-nine foods, which were selected based on their 0.9 cumulative percent contribution, and 254 foods, which were selected based on their 0.9 cumulative multiple regression coefficients, respectively, were grouped into 97 food groups according to their nutrient contents. Several popular Korean foods, which were missing from the list due to the seasonality of the survey, were included. The portion sizes were derived from the same data set. The SQFFQ covered 84.8 percent of the intake of 17 nutrients in the one day diet record data of our 326 cohort study subjects. Conclusions . The final list included 103 food items. The foods list in the SQFFQ described herein accounted for 84.8 percent of the average intake of 17 nutrients. Therefore, the list could be used for the assessment of the baseline dietary intakes of the conjoint cohort studies.

• Journal of Korea Water Resources Association
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• v.37 no.3
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• pp.233-240
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• 2004

Most of hydrological analyses in the field of water resources are launched by gathering and analyzing rainfall data. Several methods have been developed to estimate areal rainfall from point rainfall data and to fill missing or ungaged data. Thiessen and Reciprocal Distance Squared(RDS) methods whose parameters are only dependent on inter-station distance are classical work in hydrology, but these techniques do not provide a continuous representation of the hydrologic process involved. In this study, kriging technique was applied to rainfall analysis in Nakdong river basin in order to complement the defects of these classical methods and to reflect spatial characteristics of regional rainfall. After spatial correlation and semi-variogram analyses were performed to perceive regional rainfall property, kriging analysis was performed to interpolate rainfall data for each grid Thus, these procedures were enable to estimate average rainfall of subbasins. In addition, poor region of rainfall observation was analyzed by spatial interpolation error for each grid and mean error for each subbasin.

• Journal of Preventive Medicine and Public Health
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• v.57 no.4
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• pp.307-318
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• 2024

Objectives: Effective data collection and surveillance of epidemiological trends are essential in confronting the growing challenges associated with substance use (SU), especially in light of emerging trends and underreporting of cases. However, research and data are scarce regarding SU and substance use disorder (SUD) in Korea. Methods: We conducted a scoping review to identify data sources and surveillance methods used in SU research in Korea up to December 2023. This review was complemented by semi-structured consultations with experts in this area in Korea, whose feedback led to revisions of previously identified data sources and assessments. Results: Our review identified 32 publications conducting secondary analyses on existing data to examine the epidemiology of SU and SUD in Korea. Of these, 14 studies utilized clinical databases to explore the prescription patterns of addictive substances, particularly opioids. Eleven data sources showed promise for advancing SU research; however, they face substantial limitations, including a lack of available data, missing data, the absence of key variables, the exclusion of marginalized populations not captured within the clinical system, and complexities in matching individual-level data across time points and datasets. Conclusions: Current surveillance methods for SU in Korea face considerable challenges in accessibility, usability, and standardization. Moreover, existing data repositories may fail to capture information on populations not served by clinical or judicial systems. To systematically improve surveillance approaches, it is necessary to develop a robust and nationally representative survey, refine the use of existing clinical data, and ensure the availability of data on treatment facilities.

• Journal of Information Technology Applications and Management
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• v.18 no.1
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• pp.125-141
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• 2011

As the sales volume of online shops increases, the job burden in the back-offices of the online shops also increases. Order picking is the most labor-intensive operation among the jobs in a back-office and mid-size pure click online shops are experiencing the time delay and complexity in order picking nowadays while fulfilling their customers' orders. Those warehouses of the mid-size shops are based on manual systems, and as order pickings are repeated, the warehouses get a mess and lots of products in those warehouses are getting missing, which results in severe delay in order picking. To overcome this kind of problem in online clothing shops, we research a methodology to locate warehousing products. When products arrive at a warehouse, they are packed into a box and located on a rack in the warehouse. At this point, the operator should determine the box to be put in and the location on the rack for the box to be put on. This problem could be formulated as an Integer Programming model, but the branch-and bound algorithm to solve the IP model requires enormous computation, and sometimes it is even impossible to get a solution in a proper time. So, we relaxed the problem, developed a set of heuristics as a methodology to get a semi-optimum in an acceptable time, and proved by an experiment that the solutions by our methodology are satisfactory and acceptable by field managers.