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Park, Jung-Joon,Park, Heungsun,Kim, Yong-Heon,Kijong Cho Korean Society of Applied Entomology 2000 Journal of Asia-Pacific Entomology Vol.3 No.2
A sequential sampling procedure for classifying the ratio of prey/predators developed by Nyrop (1988) was examined to implement for a biological control in the greenhouse roses. The procedure was combined with a sequential density classification procedure for use in monitoring a phytophagous mite, Tetranychus urticae Koch, and a phytoseiid predator, Phytoseiuluss persimilis Athias-Henriot. This procedure was required four Parameters: Means and variances for f urticae and P persimilis, correlation coefficient between densities of prey and predates and critical ratio of prey and predator. The parameter values used in this study were 0.725 for the correlation coefficient and 10 for the critical ratio. The variances for each species were estimated using a Taylor\`s power law model. The procedure is proven to be successfully applicable to T urticae and f persimilis system in greenhouse roses at two action threshold levels of f and 10 f urticae per three-leaflet leave. The limitations and implementation of this procedure is also discussed.
On Relative-Error Forecasting of AR(1) Series Data
Park, Heungsun,Shin, Key-il,Shin, Min-Woong 한국외국어대학교 외국학종합연구센터 부설 기초과학연구소 1996 기초과학연구 Vol.5 No.-
Relative-Error Prediction Rule is applied to the stationary autoregressive time series data. With being different from the ordinary least squares forecasting, relative-error forecasting underestimates the mean response, but it minimizes relative prediction error even in AR(l) series. Monte Carlo simulation and a real data example verify these results.
Relative-Error Prediction:Nadaraya-Watson and Local Linear Regression Approaches
Park,Heungsun,Shin,Key-il 한국외국어대학교 외국학종합연구센터 부설 기초과학연구소 1999 기초과학연구 Vol.8 No.-
Relative-Error Prediction is the prediction method that minimizes the error percentage relative to the observed value, while the ordinary prediction method minimizes the prediction error istelf. In this article, we introduce a local linear smoother and Nadaraya-Watson type kernel smoothers on relative-error prediction. The presented methods are applied to a simulated data.
On Study of the Difference between GEE and Pseudolikelihood Estimation
Park, Heungsun 한국외국어대학교 기초과학연구소 2001 기초과학연구 Vol.11 No.-
The repeated measurement data analysis on Generalized Linear Models has depend on the Generalized Estimating Equation (GEE). Recently, Pseudolikelihood Estimation Method (Wolfinger and O'connell (1993), Breslow and Clayton (1993)) was adopted in Generalized Linear Mixed Models, where the random components are considered in Genralized Linear Models. The purpose of this study is to describe the difference between GEE and Pseudollikelihood Estimation in the case of repeated measurement data in Generalized Linear Models.
Use of Random Coefficient Model for Fruit Bearing Prediction in Crop Insurance
Park Heungsun,Jun Yong-Bum,Gil Young-Soo 한국통계학회 2005 Communications for statistical applications and me Vol.12 No.2
In order to estimate the damage of orchards due' to natural disasters such as typhoon, severe rain, freezing or frost, it is necessary to estimate the number of fruit bearing before and after the damage. To estimate the fruit bearing after the damages are easily done by delegations, but it cost too high to survey every insured farm household and calculate the fruit bearing before the damage. In this article, we suggest to use a random coefficient model to predict the numbers of fruit bearing in the orchards before the damage based on the tree age and the area information.
Park, Jung-Joon,Kim, Jong-Kwan,Park, Heungsun,Kijong Cho Korean Society of Applied Entomology 2001 Journal of Asia-Pacific Entomology Vol.4 No.2
The presence-absence model (PAM) was implemented to estimate the mean population density of Aphis gossypii Glover on grid-sticky traps in commercial cucumber greenhouses. The grid consisted of 4 by 6 cells (24cells per trap), each cell in size of $4\textrm{cm}^2$ (2 by 2cm). The PAM described the relationship between the number of occupied cells and the number of aphids in a natural logarithmic scale reasonably well, and most trap cases were within 95% confidence limits of the predicted model. The distribution pattern of aphids on each trap was confirmed mostly nonrandom (75.3% of total traps) by Morisita's index, whereas the pattern among the traps was random according to PAM. The time cost for estimating the mean density of aphids by the PAM method was more efficient, compared to the whole trap counting method. This study demonstrated that the cell-occupied method based on the presence-absence model could be successfully implemented for estimating mean density of aphids in cucumber greenhouses.
박흥선 한국외국어대학교 외국학종합연구센터 부설 기초과학연구소 1999 기초과학연구 Vol.7 No.-
본 논문은 변량인 모수를 포함하는 혼합모형에 대한 추정방법의 발달을 연대적으로 정리해 보고, 또한 선형모형으로부터 일반화 선형모형에 이르기까지 추정방법을 개괄적으로 다루어 봄으로써, 혼합모형에 대한 전체적인 이해를 돕고자 한다. 현재 일반화 혼합모형에 대해 제대로 정리된 서적이 없는 상태인 관계로 이와 관련된 여러 논문을 연대순으로 정리해 나가면서, 일반화선형혼합모형(Generalized Linear Mixed Model)의 활용 분야 및 앞으로의 전망도 살펴보기로 한다. This article describes the chronical development of the generalized linear mixed model(GLMM) from the conventional linear mixed model, that helps us to understand the several likelihood-base estimation methods. As there is no reference book for the generalized linear mixed model, I have reviewed the relevant papers from the origin to the up-to-date papers and summarized them in order. GLMM will be widely used not only because of it's broad application range but also because of the enhanced computing capability.
상대오차예측을 이용한 자동차 보험의 손해액 예측: 패널자료를 이용한 연구
박흥선,Park, Heungsun 한국통계학회 2021 응용통계연구 Vol.34 No.5
상대오차를 이용한 예측법은 상대오차(혹은 퍼센트오차)가 중요시되는 분야, 특히 계량경제학이나 소프트웨어 엔지니어링, 또는 정부기관 공식통계 부분에서 기존 예측방법 외에 선호되는 예측방법이다. 그 동안 상대오차를 이용한 예측법은 선형 혹은 비선형 회귀분석 뿐 아니라, 커널회귀를 이용한 비모수 회귀모형, 그리고 정상시계열분석에 이르기까지 그 범위가 확장되어 왔다. 그러나, 지금까지의 분석은 고정효과(fixed effect)만을 고려한 것이어서 임의효과(random effect)에 관한 상대오차 예측법에 대한 확장이 필요하였다. 본 논문의 목적은 상대오차예측법을 일반화선형혼합모형(GLMM)에 속한 감마회귀(gamma regression), 로그정규회귀(lognormal regression), 그리고 역가우스회귀(inverse gaussian regression)의 패널자료(panel data)에 적용시키는데 있다. 이를 위해 실제 자동차 보험회사의 손해액 자료를 사용하였고, 최량예측량과 최량상대오차예측량을 각각 적용-비교해 보았다. Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.
오인배,박흥선,신정아 高麗大學校 統計硏究所 1999 應用統計 Vol.14 No.-
본 연구는 "겨울에 눈이 많이 오면 풍년이 든다"는 속담을 통계적으로 검정하고자 하는 것을 목적으로 하고 있다. 쌀과 보리에 대한 단위 면적 당 수확량과 신적설과 적설을 반복측정모형으로 모형화 함으로써 접근하였는데, 그 결과, 눈이 많이 오면 풍년이 든다는 속담이 통계적으로 유의 하다고 결론 내릴 수 있었다. In Korean proverbs, "The big snowfall makes the big crop yield". We collected the snowfall and the crop yield data for the consecutive 9 years from 1988 to 1996; and we investigate the statistical significance of the relationship appeared in the proverb. In conclusion, the proverb is statistically valid on the rice and the barley yields.