• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.313-320
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• 2008

A typical software development life cycle consists of a series of phases, each of which has some ability to insert and detect defects. To achieve desired quality, we should progress the defect removal with the all phases of the software development. The well-known model of phase-based defect profile is Gaffney model. This model assumes that the defect removal profile follows Rayleigh curve and uses the parameters as the phase index number. However, these is a problem that the location parameter cannot present the peak point of removed defects when you apply Gaffney model to the actual situation. Therefore, Gaffney model failed to represent the actual defect profile. This paper suggests two different models: One is modified Gaffney model that introduce the parameter of Putnam's SLIM model to replace of the location parameter, the other is the growth function model because the cumulative defect profile shows S-shaped. Suggested model is analyzed and verified by the defect profile sets that are obtained from 5 different software projects. We could see from the experiment, the suggested model performed better result than Gaffney model.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.2
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• pp.120-125
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• 2010

A Monte-Carlo simulation method is proposed which can take uncertainties of scale and location parameters of Gumbel distribution into account straightforwardly in evaluating significant design wave heights with respect to return periods. The uncertainties of design wave heights may directly depend on the amounts of uncertainties of scale parameter and those distributions may be followed by Gumbel distribution. In case of that the expected values of maximum significant wave height during lifetime of structures are considered to be the design wave heights, more uncertainties are happened than in those evaluated according to return periods with encounter probability concepts. In addition, reliability analyses on the armor units are carried out to investigate into the effects of the uncertainties of design wave heights on the probability of failure. The failure probabilities of armor units to 5% damage level for 50 return periods are evaluated and compared according to the methods of taking uncertainties of design wave heights into account. It is found that the probabilities of failure may be distributed into wide ranges of bounds when the uncertainties of design wave heights are assumed to be same as those of annual maximum significant wave heights.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.31-40
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• 2009

Spatial time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations. In this paper, We estimate the parameters of spatial time autoregressive moving average (SIARMA) process by method of Gibbs sampling. Finally, We apply this method to a set of U.S. Mumps data over a 12 states region.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.375-382
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• 2010

In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.567-579
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• 2015

This study performed the non-stationary flood frequency analysis considering time-varying parameters of a probability density function. Also, return period and risk under non-stationary condition were estimated. A stationary model and three non-stationary models using Generalized Extreme Value(GEV) were developed. The only location parameter was assumed as time-varying parameter in the first model. In second model, the only scale parameter was assumed as time-varying parameter. Finally, the both parameters were assumed as time varying parameter in the last model. Relative likelihood ratio test and Akaike information criterion were used to select appropriate model. The suggested procedure in this study was applied to eight multipurpose dams in South Korea. Using relative likelihood ratio test and Akaike information criterion it is shown that the inflow into the Hapcheon dam and the Seomjingang dam were suitable for non-stationary GEV model but the other six dams were suitable for stationary GEV model. Also, it is shown that the estimated return period under non-stationary condition was shorter than those estimated under stationary condition.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.217-223
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• 1998

공통의 자유도를 갖는 $textsc{k}$개의 비중심 카이제곱분포들의 동질성을 검정하기 위하여 우선 적당한 형태의 검정방법을 제시하였다. 통상적인 방법대로, 제시된 검정방법이 주어진 유의수준을 만족시키도록 하기 위해서는, 귀무가설하에서 제 1종의 오류의 확률을 최대화하는 모수의 최소 우호적 위치(Least favorable configuration)가 유도되었으며, 이에 따라서 주어진 유의수준을 충족하는 기각치를 도표화하였다.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.229-234
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• 1996

로우버스트 추정에서 자주 사용되는 Huber의 ${\phi}$-함수의 선형 결합을 통해 새로운 재하강 ${\phi}$-함수를 도출한다. 이 함수를 사용하면 적절한 조건하에서 앞의 두 함수를 사용할 때보다 위치 모수(location parameter)에 대한 추정량의 점근분산(asymptotic variance)을 감소시킬 수 있음을 보였다.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.33-45
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• 2012

The primary interest of drug development studies is to estimate the smallest dose that shows a significant difference from the zero-dose control. The smallest dose is called the Minimum Effective dose(MED). In this paper, we suggest a nonparametric procedure to simultaneously find the MED of each group based on placements. The Monte Carlo simulation is adapted to estimate the power and the family-wise error rate(FWE) of the new procedures with those of discussed nonparametric tests to find MED.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.211-211
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• 2023

본 연구에서는 지역빈도분석을 이용하고 있는 홍수량 산정 지침에서 활용되고 있는 전국대상의 강우관소에 대한 확률강우량과 강원지역에 위치한 강우관측소만을 대상으로 산정한 확률강우량을 비교하였다. 이를 위해서 강원도 지역의 48개 지점의 지속기간별 강우자료를 수집한 후, K-means 기법을 이용하여 6개의 군집으로 구분하였다. 강원도 대부분이 산악지형임을 고려해 산악효과를 야기하는 지형인자와 강우자료의 관계를 파악하였다. 국가수자원관리종합정보시스템에서 수집한 강우자료를 사용하여 지속시간별 최대강우량과 산악효과를 야기하는 지형인자로 선정한 고도 이외에 위도, 경도를 각각 추가인자로 고려해 지역빈도분석을 수행하였다. 위 지형인자와 강우자료를 이용하여 수문학적 동질한 특성을 가지는 군집을 구성하였으며, 위도와 경도를 인자로 추가하면 더욱 강한 상관성을 보임을 알 수 있었다. 군집분석결과를 통해 모수를 추정하고 적절한 분포를 선택하였으며, 이상치검정과 적합도 검정을 통해 최종 분포를 결정하였다. 고도와 위도, 경도를 모두 고려해 이용한 지역빈도분석 결과 강원도의 실제 강우특성과 마찬가지로 고도의 높낮이에 따라 강우형태를 전국단위 지역빈도분석과 비교하였다. 최종적으로 현재 활용되고 있는 홍수량 산정 지침의 확률강우량과 강원지역에 위치한 강우관측소만을 대상으로 한 지역빈도분석의 차이의 발생원인과 강원지역에서의 특이성을 결론으로 제시하였다.