• Journal of the Korean Statistical Society
• /
• v.18 no.1
• /
• pp.26-37
• /
• 1989

When we select the t best out of k populations in the indifference zone formulation, a lower confidence bound on the probability of a correct selection is derived for families with monotone likelihood ratio. The result is applied to the normal means problem when the variance is common, and to the normal variances problem. Tables to implement the confidence bound for the normal variances problem are provided.

• The Korean Journal of Applied Statistics
• /
• v.19 no.3
• /
• pp.535-544
• /
• 2006

We consider use of Bootstrap calibration in the problem of setting a confidence interval for a linear combination of variance components. Based on the the modified large sample(MLS) method by Graybill and Wang(1980), Bootstrap Calibration is applied to improve the coverage probability of the MLS confidence bound when the experiment is balanced and coefficients of a linear combination are positive. Performance of the proposed confidence bound in small sample is investigated by simulation studies.

• Journal of Korean Society for Quality Management
• /
• v.24 no.1
• /
• pp.87-95
• /
• 1996

We construct bootstrap confidence bounds for reliability, R=P(X>Y), where X and Y are independent normal random variables. 1-way random effect models with equal variances are assumed for the populations of X and Y. We compare the accuracy of the proposed bootstrap confidence bounds and classical confidence bound for small samples via Monte Carlo simulation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.1
• /
• pp.25-28
• /
• 2011

In this paper, we study some properties of condition for fuzzy data, agrement index by ratio of area and the uniformly most powerful fuzzy test of hypothesis. Also, we suggest a confidence bound for uniformly most powerful fuzzy test. For illustration, we take the most powerful critical fuzzy region from exponential distribution by likelihood ratio and test the hypothesis of ${\chi}^2$-distribution by agreement index.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.45-46
• /
• 2019

본 논문에서는 Upper Confidence Bound (UCB)를 이용한 효율적인 패킷 스케줄링 기법을 제안한다. 기존 e-greedy 등 강화학습의 보상을 극대화 할 수 있는 행동을 선택하는 것과 다르게, 제안된 UCB를 이용한 강화학습 패킷 스케줄링 기법은 각 상태에서 행동을 선택한 횟수를 추가적으로 고려한다. 이는 보다 효율적인 강화학습의 탐구(Exploration)를 가능케 한다. 본 논문에서는 컴퓨터 시뮬레이션을 통하여 제안하는 UCB를 이용한 강화학습 패킷 스케줄링 기법이 기존의 e-greedy 및 softmax를 기반으로 한 패킷 스케줄링 기법에 비해 정확도 측면에서 향상된 정확도를 보인다.

• Journal of Korean Society for Quality Management
• /
• v.42 no.2
• /
• pp.199-208
• /
• 2014

Purpose: This paper explains how to obtain the Bayes estimates of the whole launch vehicle and of a vehicle stage, respectively, for a newly developed expendable launch vehicle. Methods: We determine the parameters of the beta prior distribution using the upper bound of the 60% Clopper-Pearson confidence interval of failure probability which is calculated from previous launch data considering the experience of the developer. Results: Probability that a launch vehicle developed from an inexperienced developer succeeds in the first launch is obtained by about one third, which is much smaller than that estimated from the previous research. Conclusion: The proposed approach provides a more conservative estimate than the previous noninformative prior, which is more reasonable especially for the initial reliability of a new vehicle which is developed by an inexperienced developer.

• Journal of Positioning, Navigation, and Timing
• /
• v.3 no.1
• /
• pp.17-23
• /
• 2014

Satellite Based Augmentation Systems (SBAS) provide ionospheric corrections at geographically five degree-spaced Ionospheric Grid Points (IGPs) and confidence bounds, called Grid Ionospheric Vertical Errors (GIVEs), on the error of those corrections. Since the ionosphere is one of the largest error sources which may threaten the safety of a single frequency Global Navigation Satellite System (GNSS) user, the ionospheric correction and integrity bound algorithm is essential for the development of SBAS. The current single frequency based SBAS, already deployed or being developed, implement the ionospheric correction and error bounding algorithm of the Wide Area Augmentation System (WAAS) developed for use in the United States. However, the ionospheric condition is different for each region and it could greatly degrade the performance of SBAS if its regional characteristics are not properly treated. Therefore, this paper discusses key factors that should be taken into consideration in the development of the ionospheric correction and integrity bound algorithm optimized for the Korean SBAS. The main elements of the conventional GIVE monitor algorithm are firstly reviewed. Then, this paper suggests several areas which should be investigated to improve the availability of the Korean SBAS by decreasing the GIVE value.

• Ocean and Polar Research
• /
• v.30 no.2
• /
• pp.149-158
• /
• 2008

The characteristic analysis of the estimated population parameters, i.e., standard deviation and error bound of coastal pollutant concentrations (hereafter PC, i.e., COD, TN, and TP concentrations), was carried out by using environmental data with different sampling frequency in Lake Shihwa and Incheon coastal zone. The results clearly show that standard deviation of the PC increases as its mean value increases. The error bounds of the annual mean values based on seasonally measured DO concentrations and PC data in Incheon coastal zone were estimated as ranges 2.26 mg/l, $0.68{\sim}0.86\;mg/l$, $0.62{\sim}0.80\;mg/l$, and $0.074{\sim}0.082\;mg/l$, respectively. In terms of annual mean of the DO concentration and PC in Lake Shihwa, the error bounds based on monthly measured data from 1997 to 2003 were also estimated as ranges 4.0 mg/l, 3.0 mg/l, $0.5{\sim}1.0\;mg/l$, and 0.05 mg/l, respectively. The error bound on the basis of real-time monitoring data is $7{\sim}13%$ only as compared to that of monthly measured data.

• Journal of KIISE:Databases
• /
• v.41 no.4
• /
• pp.256-261
• /
• 2014

The problem of top-${\kappa}$ set similarity joins finds the top-${\kappa}$ pairs of records ranked by their similarities between two sets of input records. We propose an efficient algorithm to return top-${\kappa}$ similarity join pairs using a sampling technique. From a sample of the input records, we construct a histogram of set similarity joins, and then compute an estimated similarity threshold in the histogram for top-${\kappa}$ join pairs within the error bound of 95% confidence level based on statistical inference. Finally, the estimated threshold is applied to the traditional similarity join algorithm which uses the min-heap structure to get top-${\kappa}$ similarity joins. The experimental results show the good performance of the proposed algorithm on large real datasets.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.2
• /
• pp.551-558
• /
• 2008

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. If there is many item in the association rule, much time is required. Xiong(2004) studies new method which is to compute the support of upper. They used support of upper to the $^{\theta}$. But $^{\theta}$ is subjective. In this paper, we present statistical objective criterion for efficiently identifying correlated pairs.