• Journal of Korean Society on Water Environment
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• v.23 no.2
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• pp.251-259
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• 2007

In this study three log-transformed linear regression models are compared with the focus of bias correction problem. The models are the traditional simple linear regression estimator (SL), the quasi maximum likelihood estimator (QMLE) and the minimum variance unbiased estimator (MVUE). Using such models, suspended solid loads can be estimated using the discharge - suspended solid data set that has been measured by NIER Nakdong River Water Environment Laboratory. As a result, SL shows negative bias for most values of the measured discharge range. QMLE is nearly unbiased for moderate values of the measured discharge range, but shows increasingly positive bias for either large or small value of the measured discharge range. MVUE is unbiased. It is also analyzed how the estimated regression coefficient and exponent are distributed along Nakdong river main stream.

• East Asian mathematical journal
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• v.5 no.1
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• pp.95-110
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• 1989

In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

• Journal of Korean Society for Quality Management
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• v.11 no.1
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• pp.24-29
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• 1983

A criterion is developed from the integrated sum of weighted squared bias and variance for use in selecting regressors. Some properties of this criterion are discussed and an example illustrating its use is included.

• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.357-364
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• 2001

Sometimes the observed data is too small to discriminate it from noise of the instrument. Say, the data can be recorded as below DL(Detection Level) value. Even though the data below Detection Level(BDL) is small vague, it can be resulted in wrong estimates for mean and variance. However, in practice, the BDL data is generally eliminated as N.D. (Not Detected) and do not record it in Korea. This study investigates the distributions according to the data values of ammonia concentration (NH$_3$-N) in Puyeo intake. Also we try to find out DL value and an appropriate method for the estimations of mean and variance of BDL values that can be discriminate the distributions. The DL is estimated by trial and error method. The appropriate method for the estimations of mean and variance of above the detection level(ADL)and BDL dada sets is selected, and the mean and variance are estimated. As a result, it is found that the Bias Corrected Maximum Likelihood Estimator is the most accurate method for NH$_3$-N in Puyeo intake.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.1-14
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• 2015

The standard GARCH model imposing symmetry on the conditional variance, tends to fail in capturing some important features of the data. This paper, hence, introduces the models capturing asymmetric effect. They are the EGARCH model and the GJR model. We provide the systematic comparison of volatility models focusing on the asymmetric effect of news on volatility. Specifically, three diagnostic tests are provided: the sign bias test, the negative size bias test, and the positive size bias test. This paper shows that there is significant evidence of GARCH-type process in the data, as shown by the test for the Ljung-Box Q statistic on the squared residual data. The estimated unconditional density function for squared residual is clearly skewed to the left and markedly leptokurtic when compared with the standard normal distribution. The observation of volatility clustering is also clearly reinforced by the plot of the squared value of residuals of export volume and values. The unconditional variance of both export volumes and export value indicates that large shocks of either sign tend to be followed by large shocks, and small shocks of either sign tend to follow small shocks. The estimated export volume news impact curve for the GARCH also suggests that $h_t$ is overestimated for large negative and positive shocks. The conditional variance equation of the GARCH model for export volumes contains two parameters ${\alpha}$ and ${\beta}$ that are insignificant, indicating that the GARCH model is a poor characterization of the conditional variance of export volumes. The conditional variance equation of the EGARCH model for export value, however, shows a positive sign of parameter ${\delta}$, which is contrary to our expectation, while the GJR model exhibits that parameters ${\alpha}$ and ${\beta}$ are insignificant, and ${\delta}$ is marginally significant. That indicates that the asymmetric volatility models are poor characterization of the conditional variance of export value. It is concluded that the asymmetric EGARCH and GJR model are appropriate in explaining the volatility of export volume, while the symmetric standard GARCH model is good for capturing the volatility.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.247-257
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• 2013

A variety of practical problems can be addressed in the framework of rotation (successive) sampling. The present work presents a sample rotation pattern where sampling units are drawn on two successive occasions. The problem of estimation of population variance on current (second) occasion in two - occasion successive (rotation) sampling has been considered. A class of estimators has been proposed for population variance that includes many estimators as a particular case. Asymptotic properties of the proposed class of estimators are discussed. The proposed class of estimators is compared with the sample variance estimator when there is no matching from the previous occasion. Optimum replacement policy is discussed. Results are supported with the empirical means of comparison.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.73-81
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• 2015

The paper proposes a prototype selection method and evaluates the generalization performance of standard algorithms and prototype based classification learning. The proposed prototype classifier defines multidimensional spheres with variable radii within class areas and generates a small set of training data. The nearest-neighbor classifier uses the new training set for predicting the class of test data. By decomposing bias and variance of the mean expected error value, we compare the generalization errors of k-nearest neighbor, Bayesian classifier, prototype selection using fixed radius and the proposed prototype selection method. In experiments, the bias-variance changing trends of the proposed prototype classifier are similar to those of nearest neighbor classifiers with all training data and the prototype selection rates are under 27.0% on average.

• The Research Journal of the Costume Culture
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• v.12 no.1
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• pp.28-40
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• 2004

The purpose of this study was (1) to identify the experimental bias which could appear person perception as results of development methods (2) to find out the most desirable method in developments of stimulus. The research was a quasi experiment and the subjects were 358 male and female undergraduate students by convenient sampling. The experimental instruments consisted of a set of stimulus and semantic differential scales of 7-point bi-polar adjectives. The collected data were analyzed by Principle Component Analysis, ANOVA(analysis of variance), Scheffe's multiple range test. The independent variables were developing methods of stimulus(live person, photography of real person, 2 kinds of black and white line drawing, 2 kinds of color drawing). The results were as follows. First, five factors which were potency, sociality, appearance, evaluation, activity impressional dimensions emerged to account for the methods of development of stimulus. Second, the methods of development of stimulus had significant effects on potency, sociality, appearance, activity factors. In sociality factors, the impression of photographic stimulus was the closest to the live person's impression. However in the appearance and activity impressions, significant difference existed between live person and other developing stimulus. In the potency impression, black and white line-drawing gave the highest impressional bias. In the sociability and appearance impressions, color-drawing stimulus gave the highest impressional bias. Result: On conclusion the developments of stimulus can effect on stimulus person's impressions and these effects can produce experimental biases, the photographic stimulus gave the least impressional bias. The study shows that photography of real person will produce minimize measurement error.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.65-73
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• 2000

Kernel estimator is very popular in nonparametric density estimation. In this paper we propose an estimator which reduces the bias to the fourth power of the bandwidth, while the variance of the estimator increases only by at most moderate constant factor. The estimator is fully nonparametric in the sense of convex combination of three kernel estimators, and has good numerical properties.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.515-525
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• 2010

We investigated design-based properties of the ordinary least square estimator(OLSE) and the weighted least square estimator(WLSE) in a panel regression model. Given a complex data we derive the magnitude of the design-based bias of two estimators and show that the bias of WLSE is smaller than that of OLSE. We also conducted a simulation study using Korean welfare panel data in order to compare design-based properties of two estimators numerically. In the study we found the followings. First, the relative bias of OLSE is nearly two times larger than that of WLSE and the bias ratio of OLSE is greater than that of WLSE. Also the relative bias of OLSE remains steady but that of WLSE becomes smaller as the sample size increases. Next, both the variance and mean square error(MSE) of two estimators decrease when the sample size increases. Also there is a tendency that the proportion of squared bias in MSE of OLSE increases as the sample size increase, but that of WLSE decreases. Finally, the variance of OLSE is smaller than that of WLSE in almost all cases and the MSE of OLSE is smaller in many cases. However, the number of cases of larger MSE of OLSE increases when the sample size increases.