• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.261-267
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• 2003

표본의 크기가 작은 경우 추정치의 정도에 문제가 발생한다. 본 연구에서는 대규모 조사에서의 표본을 소지역 혹은 소도메인에 할당하였을 경우 발생하는 추정치의 문제점을 해결하는 방안으로서 회귀모형을 도입하였다. 회귀모형을 기계산업 표본설계 자료에 적용하여 소지역추정의 가능성을 확인하였으며, 고전적인 추정방법과의 비교도 함께 이루어졌다.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.271-280
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• 2005

Kriging methods as traditional spatial data analysis methods and geographical weighted regression models as statistical analysis methods are compared. In this paper, we apply data from the Ministry of Environment to spatial analysis for practical study. We compare these methods to performance with monthly carbon monoxide observations taken at 116 measuring area of air pollution in 1999.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.395-403
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• 2002

In the third phase of the response surface methods, the first-order model is assumed and the curvature of the response surface is checked with a fractional factorial design augmented by centre runs. We further assume that a true model is a quadratic polynomial. To choose an optimal design, Box and Draper(1959) suggested the use of an average mean squared error (AMSE), an average of MSE of y(x) over the region of interest R. The AMSE can be partitioned into the average prediction variance (APV) and average squared bias (ASB). Since AMSE is a function of design moments, region moments and a standardized vector of parameters, it is not possible to select the design that minimizes AMSE. As a practical alternative, Box and Draper(1959) proposed minimum bias design which minimize ASB and showed that factorial design points are shrunk toward the origin for a minimum bias design. In this paper we propose a robust AMSE design which maximizes the minimum efficiency of the design with respect to a standardized vector of parameters.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.11-20
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• 2017

In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.31-31
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• 2020

본 연구는 대표적인 봄 꽃 식물인 진달래(Rhododendron mucronulatum Turcz.)의 개화시기를 예측하기 위해 지난 9년간(2011년-2019년) 주왕산 지역에 생육하는 진달래의 식물계절자료(파열·개화·개엽·만개·낙엽)와 기상자료(일평균기온·일최고기온·일최저기온)를 토대로 이탈리아 생물기상연구소(IBMET)의 Chill Day 개화 예측모형인 생물계절모형을 실시하였다. 생물계절모형에 의한 예상 발아일간 편차의 제곱을 최소로 하는 조합은 기준온도 5℃, 저온요구량과 가온요구량은 97.94로 나타났다. 즉, 휴면해제일로부터 기준온도 5℃로 Chill Day를 누적시켜 97.94에 도달하는 날짜가 낙엽~내생휴면해제일이자 내생휴면해제일~발아기간까지의 값이며, 내생휴면해제일을 기점으로 개화일까지 102.93이 개화에 필요한 가온량으로 나타났다. 2011년부터 2019년까지 개화예상일을 기상청 회귀모형을 실관측기온에 적용한 결과 오차는 MAE=1.44이며, 생물계절모형을 적용할 경우 오차는 MAE=1.39, 기준온도 5℃일 경우 MAE=4.23, 기준온도 6℃일 경우 MAE=5.47, 기준온도 7℃일 경우 MAE=5.05로 나타나 생물계절에 의한 관측과 기상청의 회귀모형이 가장 유사한 것으로 나타났다. 가장 최근인 2018년과 2019년의 기상청 회귀모형와 생물계절모형의 개화 예측일을 비교한 결과, 2018년의 경우 청송지역의 진달래는 기상청 회귀모형에서 3월 30일 전후로 개화를 예상하였고 생물계절모형은 기준온도 5℃에 적용할 경우 내생휴면일에 가장 근접한 날은 3월 26일이였으며 이를 기준으로 가온량의 합이 102.93에 가깝게 되는 날인 4월 2일을 전후로 개화를 예측하였다. 실제 청송 주왕산의 진달래는 4월 3일에 개화를 시작하여 생물계절모형과 매우 유사함을 확인하였다. 2019년의 경우 청송지역의 진달래는 기상청 회귀모형에서 3월 25일 전후로 개화를 예상하였고 생물계절모형은 기준온도 5℃에 적용할 경우 내생휴면일에 가장 근접한 날은 3월 8일이였으며 이를 기준으로 가온량의 합이 102.93에 가깝게 되는 날인 3월 29일을 전후로 개화를 예측하였다. 실제 청송 주왕산의 진달래는 4월 5일에 개화를 시작하여 오히려 생물계절모형과 더욱 유사함을 확인하였다.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.91-102
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• 2007

The major purpose of this article is to formulate a class of Space Time Autoregressive Moving Average(STARMA) model and Space Time Bilinear model(STBL), to discuss some of the their statistical properties such as model, identification approaches, some procedure for estimation and the predictions, and to compare the STARMA model with the STBL model. For illustration, The Mumps data reported from eight city & provinces monthly over the years 2001-2006 are used and the result from STARMA and STBL model are compared with using SSF(Sum of Square Prediction Error).

• Journal of Animal Science and Technology
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• v.48 no.2
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• pp.151-160
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• 2006

This study was conducted to investigate the genetic characteristics of growth stages in Hanwoo, to provide useful information in farm management decisions. Data were taken from the nucleus herds of three farms, Namwon, Daegwalyong and Seosan, comprising 27,647 cows, 14,744 bulls, and 1,290 steers in between 1980 and 2004. According to the growth curve by year, the residuals for cows and bulls were 68.49 and 54.29, respectively, under the Gompertz model. The values were lower than in other years. Parameters, A, b and k were estimated as 423.6±5.8, 2.387±0.064 and 0.0908±0.0033 in cows and 823.3±15.3, 3.584±0.070, 0.1139±0.0032 in bulls, respectively. The fitness was higher under the Gompertz model than under the logistic model: monthly and daily estimation for cows were 379.3±7.509, 2.499±0.057, 0.114±0.0045 and 367.1±1.9003, 2.3983±0.012, 0.004±0.00003, respectively. Estimated residual mean squares were 31.85 and 998.4 in their respective models. Monthly and daily estimation of bulls were 834.6±22.00, 3.319±0.062, 0.104±0.0037 and 796.0±6.128, 3.184±0.014, 0.003±0.00003, respectively. Estimated residual mean square were 66.18 and 2106.5. Monthly and daily estimation of steers were 1049.1±144.2, 3.024±0.008, 0.067±0.0096 and 1505.1±176.6, 2.997±0.067, 0.001±0.0001, relatively. Squares, 186.0 and 1119.1. In terms of growth characteristic estimated by Gompertz model, body weight for cows and bulls were 139.53kg and 307.03kg, and the daily gains were 0.52kg and 1.04kg, respectively. Body weight for steers was 385.94kg at the inflection point. Body weight gain was 0.84kg in both models. Our results showed that cows had lower mature weight and daily weight gain, and reached the inflection point earlier than bulls or steers.

• Journal of Animal Science and Technology
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• v.46 no.5
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• pp.707-718
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• 2004

This study was conducted to evaluate the adequacy of an alternative a.m. - p.m. testing scheme for milk yield in comparison with the official test method based on weighing two milkings within 24 h. A total of 8,309 p.m. milking weights and 6,767 am. milking weights from 72 Holstein cows raised at N.L.R.I. were collected between October 2000 and November 2001. Seven statistical models were fitted to the data to derive formulas for estimating daily milk yields from morning or evening yields. In general, use of evening milkings less accurately estimated than did use morning rnilkings. Although the models do not differ much in the correlations between estimated and true daily milk yields, systematic under- and overestimation of daily milk yields were observed in all models with the exception of model 7, which accounted for heterogeneous variances by parity class, milking interval class, and lactation stage by fitting separate regression formulas within each combination of three factors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.