• Korean Journal of Mathematics
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• 제22권3호
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• pp.529-552
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• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• 응용통계연구
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• 제37권4호
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• pp.467-484
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• 2024

경시적 자료는 같은 개체를 반복 측정함으로써 시간의 흐름에 따른 반복 측정된 자료들 간의 상관관계가 존재한다. 따라서 경시적 자료분석에서는 이 상관관계를 분석할 때 개체 내 상관관계와 개체 간 변동성 모두를 고려해야 한다. 본 논문에서는 경시적 이진 자료를 분석하기 위한 모형 중 공변량의 모집단 평균 효과의 추정을 위해 주변화 모형에 집중하고자 한다. 경시적 이진 자료분석을 위한 주변화 모형으로는 주변화 임의효과, 주변화 전이, 주변화 전이 임의효과 모형이 있으며, 본 논문에서 이들 모형을 먼저 고찰하고, 그리고 모형들의 성능을 비교하기 위해 결측치가 없는 자료와 결측치가 있는 자료로 나눠서 모의실험을 진행한다. 모의실험에서 자료에 결측치가 있는 경우에 자료가 생성된 모형에 따른 성능 차이가 있음을 확인하였다. 마지막으로 주변화 모형을 이용하여 한국의료패널자료를 분석한다. 한국의료패널자료는 반응변수로 주관적 불건강 응답을 이진변수로 고려하였고, 여러 설명변수를 가진 모형을 비교하고 가장 적합한 모형을 제시한다.

• Journal of the Korean Data and Information Science Society
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• 제8권2호
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• pp.247-260
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• 1997

The quasi-likelihood models which greatly widened the scope of generalized linear models are widely used in data analysis where a likelihood is not available. Since a quasi-likelihood may not appear to be an ordinary likelihood for any known distribution in the natural exponential family, to fit the quasi-likelihood models the standard statistical packages such as GLIM, GENSTAT, S-PLUS and so on may not directly applied. SAS/IML is very useful for fitting of such models. In this paper, we present simple SAS/IML(version 6.11) program which helps to fit and analyze the quasi-likelihood models applied to the leaf-blotch data introduced by Wedderburn(1974), and the problem with deviance useful generally to model checking is pointed out, and then its solution method is mention through the data analysis based on this quasi-likelihood models checking.

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• 한국측량학회지
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• 제39권5호
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• pp.297-311
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• 2021

Around the world, there is an increasing interest in Digital Twin cities. Although geospatial data is critical for building a digital twin city, currently-established spatial data cannot be used directly for its implementation. Integration of geospatial data is vital in order to construct and simulate the virtual space. Existing studies for data integration have focused on data transformation. The conversion method is fundamental and convenient, but the information loss during this process remains a limitation. With this, standardization of the data model is an approach to solve the integration problem while hurdling conversion limitations. However, the standardization within indoor space data models is still insufficient compared to 3D building and city models. Therefore, in this study, we present a comparative analysis of data models commonly used in indoor space modeling as a basis for establishing a generic indoor space feature model. By comparing five models of IFC (Industry Foundation Classes), CityGML (City Geographic Markup Language), AIIM (ArcGIS Indoors Information Model), IMDF (Indoor Mapping Data Format), and OmniClass, we identify essential elements for modeling indoor space and the feature classes commonly included in the models. The proposed generic model can serve as a basis for developing further indoor feature models through specifying minimum required structure and feature classes.

• 품질경영학회지
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• 제25권1호
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• pp.44-55
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• 1997

In order to determine procedures for a, pp.opriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected according to data characteristics. Three different growth curve models were fitted onto data sets in an attempt to determine which growth curve models achieved the best forecasts for types of growth data. The analysis of the results gives rise to an a, pp.oach for selecting a, pp.opriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the goodness of fit test.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.585-594
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• 2003

It has been generally recognized that conventional binomial or Poisson model provides poor fits to the actual correlated binary data due to the extra-binomial variation. A number of generalized statistical models have been proposed to account for this additional variation. Among them, beta-binomial, correlated-binomial, and modified-binomial models are binomial-related models which are frequently used in modeling the sum of n correlated binary data. In many situations, it is reasonable to assume that n correlated binary data are exchangeable, which is a special case of correlated binary data. The sum of n exchangeable correlated binary data is modeled relatively well when the above three binomial-related models are applied. But the estimation results of correlation coefficient turn to be quite different. Hence, it is important to identify which model provides better estimates of model parameters(success probability, correlation coefficient). For this purpose, a small-scale simulation study is performed to compare the behavior of above three models.

• 산업공학
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• 제18권4호
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• pp.418-430
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• 2005

We establish an efficient strategy for construction and operation of data consolidation center for multi-organization in public sector. First, we introduce important concepts on data consolidation center in public sector, and draw some success factors by analyzing several foreign and domestic cases. Second, we construct all the possible logical operational models of the center and investigate the properties and feasibility of the models. Third, we suggest a virtual operational environment for the two representative models selected by feasibility criteria among the possible logical models, and compare the two models in terms of operational cost. We also utilize AHP methodology to evaluate qualitative opinions on the two models from several experts in public information systems. As a result, we find the best alternative is the case in which all infrastructure and facilities for the center are provided by government, and common essential IT operations are integrated, associated data are consolidated and the whole operational work are outsourced to specialized IT operations service providers.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.622-625
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• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.

• 한국IT서비스학회지
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• 제12권3호
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• pp.311-321
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• 2013

In the community of software engineering, diverse modeling languages are used for representing all relevant information in the form of models. Also many different models such as business model, business process model, product models, interface models etc. are generated through software life cycles. In this situation, models need to be integrated for enterprise integration and enhancement of software productivity. Researchers propose rebuilding models by a specific modeling language, using a intemediate modeling language and using common reference for model integration. However, in the current approach it requires a lot of cost and time to integrate models. Also it is difficult to identify common objects from several models and to update objects in the repository of common model objects. This paper proposes software model integration using metadata model based on Linked data. We verify the effectiveness of the proposed approach through a case study.