• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.713-722
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• 2009

The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.103-111
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• 2003

Kernel machines are used widely in real-world regression tasks. Kernel ridge regressions(KRR) and support vector machines(SVM) are typical kernel machines. Here, we focus on two types of KRR. One is inductive KRR. The other is transductive KRR. In this paper, we study how differently they work in the interpolation and extrapolation areas. Furthermore, we study prediction interval estimation method for KRR. This turns out to be a reliable and practical measure of prediction interval and is essential in real-world tasks.

• Proceedings of the Safety Management and Science Conference
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• 2009.04a
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• pp.365-370
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• 2009

The present study proposes the various hypothesis tests and interval estimations for homogeneous and heterogeneous populations in the continuous and discrete process. These results can be used to complement the QC story 15 steps for quality improvement circle and DMAIC processes for six sigma innovation.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.451-459
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• 2003

The present paper suggests a method to estimate confidence interval using SV(Support Vector) in LS-SVM(Least-Squares Support Vector Machine). To get the proposed method we used the fact that the values of the hessian matrix obtained by full data set and SV are not different significantly. Since the suggested method implement only SV, a part of full data, we can save computing time and memory space. Through simulation study we justified the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.449-455
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.217-230
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• 2006

e discuss proper confidence intervals for interval estimation of a low binomial proportion. A large sample surveys are practically executed to find rates of rare diseases, specified industrial disaster, and parasitic infection. Under the conditions of 0 < p ${\leq}$ 0.1 and large n, we compared 6 confidence intervals with mean coverage probability, root mean square error and mean expected widths to search a good one for interval estimation of population proportion p. As a result of comparisons, Mid-p confidence interval is best and AC, score and Jeffreys confidence intervals are next.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.167-183
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• 1995

The objective of study is to evaluate highway capaicty estimation alternative and to develop capacity from statistical distribution of observed traffic flow. Speed-Volume relation is analyzed from vehicle's headway distribution eliminating the long headway by confidence intervals 99%, 95%, 90%. Capacity estimate alternatives were evaluated from 95% , 90%, 85% level of cummulative distribution of observed hourly traffic flow adjusted to confidence intervals. The result of investigation revealed that maximum hourly rate of flow is 2, 130pcu at confidence interval of 995, 2, 233pcu at 95%, 2, 315pcu at 90% respectively. Compared to the capacity of 2, 200pcu per hour per lane used in HCM and KHCM(Korea Highway Capacity Manual), capa챠y appears to correspond to confidence interval of 95%. Using the traffic flow rate at confidence interval of 95% the maximum hourly flow rate is 2, 187pcu at 95% of cummulative volume distribution, 2, 153pcu at 90%, 2, 215pcu at 85%. The study suggests that raional capacity esimation alternative is to take the 95% of cummulative distribution of observed hourly traffic flow at 95% confidence headway interval eliminating 5% long headway.(i.e. 95-95 rule)

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.42-46
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• 2009

Das et al. [EJOR 117(1999) 100-112] discussed the real valued solution procedure of the multiobjective transportation problem(MOTP) where the cost coefficients of the objective functions, and the source and destination parameters have been expressed as interval values by the decision maker. In this note, we consider the interval valued solution procedure of the same problem. This problem has been transformed into a classical multiobjective transportation problem where the constraints with interval source and destination parameters have been converted into deterministic ones. Numerical examples have been provided to illustrate the solution procedure for this case.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1038-1041
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• 2010

오늘날 대부분의 네트워크는 수십 Gb/s 를 지원하는 광네트워크다. 이러한 고속 네트워크에서 대부분 TCP 전송 프로토콜을 사용하고 있다. TCP 전송 프로토콜은 AIMD 방식의 특성으로 인해 고속 네트워크에 적합하지 않다. 반면 UDT 는 DAIMD 방식을 사용하기 때문에 고속 네트워크에서 가용대역폭을 충분히 활용 가능하다. 그러나 UDT 는 sync-interval 간격으로 rate control 이 이루어 지기 때문에 sync-interval 동안 가용 대역폭을 충분히 활용하지 못하는 문제점이 발생한다. 본 논문에서는 RTT 예측을 통한 sync-interval 구간에서의 rate control 기법을 제안한다. sync-interval 구간 동안 RTT 예측을 통해 rate control 을 함으로써 기존 UDT에 비해 고속 네트워크 환경에서 보다 빠르게 가용대역폭을 활용할 수 있다. 네트워크 시뮬레이션 결과 기존 UDT 에 비해 throughput 및 안정성이 향상되었다.