• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.321-327
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• 2013

Bayesian and empirical Bayesian methods have become quite popular in the theory and practice of statistics. However, the objective is to often produce an ensemble of parameter estimates as well as to produce the histogram of the estimates. For example, in insurance pricing, the accurate point estimates of risk for each group is necessary and also proper dispersion estimation should be considered. Well-known Bayes estimates (which is the posterior means under quadratic loss) are underdispersed as an estimate of the histogram of parameters. The adjustment of Bayes estimates to correct this problem is known as constrained Bayes estimators, which are matching the first two empirical moments. In this paper, we propose a way to apply the constrained Bayes estimators in insurance pricing, which is required to estimate accurately both location and dispersion. Also, the benefit of the constrained Bayes estimates will be discussed by analyzing real insurance accident data.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1222-1223
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• 2008

본 논문에서는 1분에 최대 200 개의 위치보고를 처리하여야 하는 제주 택시 텔레매틱스 시스템에서 보다 효율적인 트래킹과 택시 배차를 위해 맵 매칭에 의해 차량이 위치한 도로를 검색하는 모듈을 설계하고 구현한다. 구현된 모듈은 위치기반 서비스에 있어서 저비용 효율적으로 맵 매칭을 구현하기 위해 쉐이프 파일을 직접 순차적으로 처리하고 보고지점과 세그먼트들로 이루어진 삼각형의 면적을 기반으로 매칭 링크를 검색한다. 이 기능은 차량의 진행방향을 판단하거나 도로 내에서의 위치 비율을 계산하는데 용이하며 다양한 부가 정보를 생성할 수 있다. 구현된 시스템에서 실제 히스토리 데이터에 대해 맵 매칭을 수행한 결과 95% 이상 해당 링크를 검색하였다.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.43-52
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• 2014

In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.

• Asian Journal for Public Opinion Research
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• v.10 no.3
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• pp.200-229
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• 2022

The purpose of this study is to analyze the effects of youth job policy measures, set forth in Korea's 2016-2020 Third Basic Plan for Low Fertility and Aging Society (December 2015), on marriage and fertility among young people. Based on the results, we provide theoretical explanations for the findings and suggest policy alternatives to overcome the low fertility phenomenon in Korea. Previous studies have shown that employment is an important factor for marriage among youth, and a job policy could increase marriage and fertility rates. To test this assumption, we performed an exact matching between Statistics Korea's Employee-Enterprise Linkage DB and the Newlyweds DB from 2011 to 2019, in order to identify all young people aged 15-34. Then, linear spline regression analysis was used to examine the impact of the youth job policy on marriage and fertility. Comparing the period before the implementation of the employment policy (2011-2015) and after (2016-2019), the fertility rate increased as the number of young people looking for work increased. In addition, it was found that these impacts were greater after the implementation of the measures (2016-2019) than before (2011-2015). It is interesting to note that job growth among young people did not lead to an increase in marriage. However, the number of births significantly increased when young people who occupy jobs got married, which seems to be related to the delay in marriage among young people who are employed. Survey results about the intentions to marry and views on fertility are utilized for the explanation of the study results.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.675-685
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• 2011

This paper proposes a job matching function that calculates the job matching probability of a job-seeker to an employer taking the working conditions of a job-seeker and an employer into account. In addition, this study analysis the degree of centrality that means interactions of a job-seeker and an employer utilizing social network analysis. The results are follows. First, a degree of centrality is found to be severely concentrated in certain job-seekers or certain employers; in addition, there are many job-seekers and employers who have no matching results. Second, according to decision tree analysis, characteristics of a job-seeker that influences the degree of centrality are gender, age and degree of education in order of importance. The characteristics of a employer that influences the degree of centrality are proposed salary, industry classification and firm size in order of importance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.583-599
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• 2015

We apply the Bayesian matching pursuit (BMP) algorithm to the estimation of time-frequency selective channels in orthogonal frequency division multiplexing (OFDM) systems. By exploiting prior statistics and sparse characteristics of propagation channels, the Bayesian method provides a more accurate and efficient detection of the channel status information (CSI) than do conventional sparse channel estimation methods that are based on compressive sensing (CS) technologies. Using a reasonable approximation of the system model and a skillfully designed pilot arrangement, the proposed estimation scheme is able to address the Doppler-induced inter-carrier interference (ICI) with a relatively low complexity. Moreover, to further reduce the computational cost of the channel estimation, we make some modifications to the BMP algorithm. The modified algorithm can make good use of the group-sparse structure of doubly selective channels and thus reconstruct the CSI more efficiently than does the original BMP algorithm, which treats the sparse signals in the conventional manner and ignores the specific structure of their sparsity patterns. Numerical results demonstrate that the proposed Bayesian estimation has a good performance over rapidly time-varying channels.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• Journal of Korean Society for Quality Management
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• v.31 no.4
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• pp.219-225
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• 2003

Two­dimensional warranty data can be modelled using two different approaches： two­dimensional point process and one­dimensional point process with usage as a function of age. The first approach has three different models. First of all, bivariate model is appealing but is not appropriate for explaining warranty claims. Next, the rest of the two models (marked point process, and counting and matching on both directions independently) are more appropriate for explaining warranty claims. However, the second one (counting and matching on both directions independently) assumes that the two variables (variables representing the two­dimensions) are independent. Last of all, one­dimensional point process with usage as a function of age is also promising to explain the two­dimensional warranty claims. But the models or variations of them need more investigation to be applicable to real warranty claim data.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.304-309
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• 1999

In this paper, we presents a new disparity map refinement algorithm using statistical characteristics of disparity map and edge information. The proposed algorithm generate a refined disparity map using disparity maps which are obtained from area and feature-based Stereo Matching by selecting a disparity value of edge point based on the statistics of both disparity maps. Experimental results on aerial stereo image show the better results than conventional fusion algorithms in the disparity error. This algorithm can be applied to the reconstruction of building image from the high resolution remote sensing data.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.405-414
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• 2002

We consider the problem of estimating the error variance of in a two-way mixed-effects ANOVA model using noninformative priors. First, we derive Jeffreys' prior, a reference prior, and matching priors. We then provide marginal posterior distributions under those noninformative priors. Finally, we provide graphs of marginal posterior densities of the error variance and credible intervals for the error variance in two real data set and compare these credible intervals.