• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.96-110
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• 1989

To suggest the fundamental data for the estimation of crop evaportranspiration by the ca- lculated coefficients for estimating the radiation suitable to the different regions of korea in application of Penman equcation, the daily data such as sc(skycover), n(actual sunshine hours), N(possible sunshine hours), Rs(horizontal solar radiation) and Ra(extraterrestial solar radiation) for 10 years (from 1977 to 1986) collected from 19 meteorological stations were analysed. The results are summarized as follows : 1. The coefficients a, b and c for estimating the radiation taken by the regression method with the daily and monthly mean data of the skycover and the ratio of Rs to Ra were shown as a=0.619, b= -0.0202, c= -0.0023 and a=0.64, b=0.0377 c=0.0001 in ave- rage respectively. 2. The coefficients a and b for estimating the radiation analysed by the regression and arithmetic method from the daily ratio of sunshine hours and Rs to Ra were shown as a= 0.157, b= 0.529, and a=0.119, b= 0.726 in average, respectively. 3. The coefficients a and b for estimating the radiation calculated by the regression me- thod based on the monthly ratio of sunshine hours and radiation were shown as a=0. 319 and b= 0.557 in average. 4. The values of a and b for estimating the radiation taken from the relationship between the daily ratio of sunshine hours and radiation showed high significance level. 5. The standard deviation and the coefficient of variance between the radiation calculated from the coefficients by the regression and arithmetic method with the daily data and the actual radiation were analysed and compared to the results by the coefficients of the modified Penman method (a=0.18, b=0.55) and by those of the F.A.O inodified Penman method(a=0.25, b=0.5). The standard deviation and the coefficient of varia- nce by the regression method in this study showed the lowest value. 6. From the above results, it is suggested that regression method using the coefficients taken from the relationship between the ratio of sunshine hours and the ratio of radia- tion based on the daily data has the highest accuracy in estimating the radiation. 7. The average reference crop evapotranspiration estimating by the modified Penman me- thod using the coefficients a and b derived by the regression method from the daily meterological data was closer to the actual evapotsranspiration of grass measured in Suwon area than the estimated evapotranspiration by the modified Penman method and the F.A.O modified Penman method.

• Bulletin of the Society of Naval Architects of Korea
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• v.26 no.4
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• pp.14-26
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• 1989

This paper describes the method of statistical analysis and its programs for predicting the ship's powering performance. The equation for the wavemaking resistance coefficient is derived as the sectional area coefficients by using the wavemaking resistance theory and its regression coefficients are determined from the regression analysis of the model test results. The equations for the form factor, wake franction and thrust deduction fraction are derived by purely regression analysis of the principal dimensions, sectional area coefficients and model test results. The statistical analyses are performed using the various descriptive statistic and stepwise regression analysis techniques. The powering performance prognosis program is developed to cover the prediction of resistance coefficients, propulsive coefficients, propeller open-water efficiency and various scale effect corrections.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.251-261
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• 2004

The paper focuses primarily on the standard linear multiple regression model where the parameter of interest is a ratio of two regression coefficients. The general model includes the calibration model, the Fieller-Creasy problem, slope-ratio assays, parallel-line assays, and bioequivalence. We provide an orthogonal transformation (cf. Cox and Reid (1987)) of the original parameter vector. Also, we give some remarks on the difficulties associated with likelihood based confidence interval.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.355-363
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• 2002

Using the Buckley-James method, we construct bootstrap confidence intervals for the regression coefficients under the censored data. And we compare these confidence intervals in terms of the coverage probabilities and the expected confidence interval lengths through Monte Carlo simulation.

• Journal of the Korean Statistical Society
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• v.5 no.1
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• pp.3-18
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• 1976

To test simultaneous significance of more than two linear regression coefficients, we can consider multivariate-t tests with critical regions in t-space instead of F-tests where t-values are t-statistics of significance tests of one coefficient. In this paper bivariate-t distributions and bivariate-t tests of two coefficients such as maxmod, minmod, one-tailed maxmod and one-tailed minmod tests are studied. Through the calculation of powers of test, it is learned that in some cases bivariate-t test are more powerful than F-tests.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1204-1209
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• 1990

This paper offers the theory and method for regression analysis of the regression model with operational parameter variables based on the fundamentals of mathematical statistics. Regression coefficients are usually constants related to the problem of regression analysis. This paper considers that regression coefficients are not constants but the functions of some operational parameter variables. This is a kind of method of two-step fitting regression model. The second part of this paper considers the experimental step numbers as recursive variables, the recursive identification with unknown operational parameter variables, which includes two recursive variables, is deduced. Then the optimization and the recursive identification are combined to obtain the sequential experiment optimum design with operational parameter variables. This paper also offers a fast recursive algorithm for a large number of sequential experiments.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.73-84
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• 2002

Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.251-260
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• 2009

In this study, we propose a new estimation method based on autocovariance for selecting optimal estimators of the regression coefficients in the simple linear regression model. Although this method does not seem to be intuitively attractive, these estimators are unbiased for the corresponding regression coefficients. When the exploratory variable takes the equally spaced values between 0 and 1, under mild conditions which are satisfied when errors follow an autoregressive moving average model, we show that these estimators have asymptotically the same distributions as the least squares estimators. Additionally, under the same conditions as before, we provide a self-contained proof that these estimators converge in probability to the corresponding regression coefficients.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.