• Genomics & Informatics
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• v.19 no.4
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• pp.36.1-36.11
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• 2021

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.85-92
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• 1991

This paper describes the trend and inherent problems of the current statistical packages, and statistical expert system is suggested as an alternative to the conventional statistical packages. The paper then describes the components and characteristics of statistical expert system, and suggests a strategy to choose development tools to build a system.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.531-541
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• 2001

In this study, we compare the capabilities of the data mining tools of the most updated version objectively and provide the useful information in which enterprises and universities chose them. In particular, we compare the SAS/Enterprise Miner 3.0, SPSS/Clementine 5.2 and IBM/Intelligent Miner 6.1 which are well known and easily gotten.

• Proceedings of the Safety Management and Science Conference
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• 2010.04a
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• pp.509-517
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• 2010

The paper proposes the misuse types of statistical quality tools according to the kind of data and the number of population in DMAIC process of six sigma. The result presented in this paper can be extended to the QC story 15 steps of QC circle. The study also provides the improvement methods about control chart, measurement system analysis, statistical difference, and practical equivalence.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.77-82
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• 2001

• Communications for Statistical Applications and Methods
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• v.30 no.5
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• pp.437-451
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• 2023

Generalized linear models and generalized linear mixed models (GLMMs) are fundamental tools for predictive analyses. In insurance, GLMMs are particularly important, because they provide not only a tool for prediction but also a theoretical justification for setting premiums. Although thousands of resources are available for introducing GLMMs as a classical and fundamental tool in statistical analysis, few resources seem to be available for the insurance industry. This study targets insurance professionals already familiar with basic actuarial mathematics and explains GLMMs and their linkage with classical actuarial pricing tools, such as the Buhlmann premium method. Focus of the study is mainly on the modeling aspect of GLMMs and their application to pricing, while avoiding technical issues related to statistical estimation, which can be automatically handled by most statistical software.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.619-634
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• 2007

It is well known that graphical display is critical to data analysis. A lot of research for data visualization has been done, so many effective graphical tools are now available. With the proper use of these graphical tools, we can penetrate the complex structure of data set easily. To enjoy the benefit of the powerful graphical display, the choice of the statistical software is very crucial. R is a popular open source software tool for statistical analysis and graphics, and can provide the very powerful graphics facilities. Moreover, many researchers believe that R is the best software for statistical graphics. In this paper, we would like to discuss what we teach and how we teach in statistical graphics course using R.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.327-345
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• 2001

New statistical methods are ended to analyzed data in a multi-scale way. Some multi-scale extensions of stand methods, including novel visualization using dynamic graphics are proposed. These tools are used to explore non-standard structure in internet traffic data.

• Journal of Korean Society for Quality Management
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• v.37 no.2
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• pp.68-77
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• 2009

There are many quality control(QC) tools useful for solving quality problems. In this paper, QC 7 tools, new QC 7 tools, and other quality tools are first compared with respect to their frequency of use. We suggest an integrated problem-solving procedure to systematically deal with various quality problems. For each step a streamlined flow chart is presented to help the practitioners to adopt relevant tools depending on certain situations they face. The procedure will help quality practitioners solve field quality problems.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.52-67
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• 2004

Successful implementation of statistical process control techniques requires for operational definitions and precise measurements. Nevertheless, very often analysts can dispose of process data available only by linguistic terms, that would be a waste to neglect just because of their intrinsic vagueness. Thus a hybrid approach, which integrates fuzzy set theory and common statistical tools, sounds useful in order to improve effectiveness of statistical process control in such a case. In this work, a fuzzy approach is adopted to manage linguistic information, and the use of a Chi-squared control chart is proposed to monitor process performance.