• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.255-264
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• 2008

It is common in practice to use mean squared error(MSE) for prediction. Recently, Park and Shin (2005) and Jones et al. (2007) studied prediction based on mean squared relative error(MSRE). We proposed a new nonparametric way of prediction based on MSRE substituting Jones et al. (2007) and provided a small simulation study which highly supports the proposed method.

• 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.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.685-694
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• 1998

임상실험이나 신뢰성공학 분야에서 임의 중단자료를 이용한 비모수적 신뢰도 추정량으로 Kaplan-Meier 추정량과 Nelson형 추정량이 많이 사용되고 있다. 그러나 Nelson형 추정량은 평균제곱오차의 관점에서 Kaplan-Meier 추정량보다 추정능력이 우수한 반면 편의는 신뢰도가 감소함에 따라 양의 방향으로 점증하는 소표본 특성을 갖는다. Nelson형 추정량의 이러한 특성 때문에 신뢰도의 함수로 표현되는 잔여수명 분위수함수 등의 추정시에는 평균제곱오차의 관점에서 Kaplan-Meier 추정량보다 추정능력이 떨어짐을 볼 수 있다. 이러한 점을 고려하여 이 두 추정량을 가중평균으로 통합한 새로운 비모수적 신뢰도 추정량을 제안하고 추정량의 특성을 비교 분석하였다.

• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.325-330
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• 2018

This paper, following the shape parameters of the minimax distribution, describes the special form of the beta distribution, the Minimax distribution, as a function of the shape parameters for the software reliability model based on the non-homogeneous Poisson process. Characteristics and usefulness were discussed. As a result, the case of the shape parameter 1 of Minimax distribution than less than and greate in mean squared error is the smallest, in determination coefficient, appears to be high, the shape parameter 1 of Minimax distribution regard as an efficient model. The estimated determination coefficient of the proposed model is estimated to be more than 95%, which is a useful model in the field of software reliability. Through this study, software design and users can identify the software failure characteristics using mean square error, decision coefficient, and confidence interval can be used as a basic guideline.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.301-310
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• 1997

본 연구에서는 비(ratio)에 대한 2차잭나이프 추정량을 제안하고, 그것의 편의와 분산이 집단의 수에 대한 감소함수임을 보인다. 또한, 이 추정량의 우수성을 편의와 평균제곱오차의 측면에서 기존의 추정량과 비교 분석한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.201-202
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• 2021

본 논문에서는 다중 엑세스 포인트 환경에서 전송전력, 수신필터, 엑세스 포인트 선정 최적화 알고리즘을 제안한다. 최종목적은 신호대간섭비를 유지하면서, 전송전력의 총합을 최소화하는 것이다. 증명을 통해서 제안하는 알고리즘은 최소전력에 수렴함을 보인다. 제안하는 알고리즘이 기존에 제안되었던 두 개의 알고리즘인 1)전송전력과 최소제곱평균오차(MMSE) 수신필터 최적화 알고리즘, 2) 전송전력 최적화 알고리즘보다 전송전력 소모량에서 성능이 우수함을 실험을 통해서 확인하였다.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.341-353
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• 1993

As an estimator of the conditional probability of discovering a new species at the next observation after a sample of certain size is taken, the one proposed by Good(1953) has been most widely used. Recently, Clayton and Frees(1987) showed via simulation that their nonparametric maximum likelihood estimator(NPMLE) has smaller MSE than Good's estimator when the population is relatively nonuniform. Lee(1989) proved that their conjecture is asymptotically true for truncated geometric population distributions. One shortcoming of the NPMLE, however, is that it has a considerable amount of negative bias. In this study we proposed a bias-corrected version of the NPMLE for virtually all realistic population distributions. We also showed that it has a smaller asymptotic MSE than Good's extimator except when the population is very uniform. A Monte Carlo simulation was performed for small sample sizes, and the result supports the asymptotic results.

• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.117-130
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• 2015

A least squares method for adjusting the horizontal network satisfies the conditions which is minimizing the sum of the squares of errors based on probability theory. This research compared accuracy of 3rd cadastral control points adjusted by traditional and least square method with respect to the result of Network-RTK. Test results showed the least square method more evenly distribute closure error than traditional method. Mean errors of least square and traditional adjusting method are 2.7cm, 2.2cm respectively. In addition, blunder in angle observations can be detected by comparing position errors which calculated by forward and backward initial coordinates. However, distance blunder cannot offer specific observation line occurred mistake because distance error propagates several observation lines which have similar directions.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.729-738
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• 2015

In this paper, we compare the robustness to the assumption of normality of the single control charts to control the mean and variance simultaneously. The charts examined were semicircle control chart, max chart and MSE chart with Shewhart individuals control charts. Their in-control and out-of-control performance were studied by simulation combined with computation. We calculated false alarm rate to compare among single charts by changing subgroup size and shifting mean of quality characteristics. It turns out that max chart is more robust than any of the others if the process is in-control. In some cases max chart and MSE chart are more robust than others if the process is out-of-control.