• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1045-1054
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• 2012

Support vector machine is known to be the very useful statistical method in classification and nonlinear function estimation. In this paper we propose a monotone support vector regression (SVR) for the estimation of monotonically decreasing function. The proposed monotone SVR is applied to smooth the Kaplan-Meier estimate of survival function. Experimental results are then presented which indicate the performance of the proposed monotone SVR using survival functions obtained by exponential distribution.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1253-1262
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• 2016

When there exists a change-point in hazard function, it should be estimated for exact parameter or hazard estimation. In this research, we compare the hazard change-point estimators. Matthews and Farewell (1982) parametric change-point estimator is based on the likelihood and Zhang et al. (2014) nonparametric estimator is based on the Nelson-Aalen cumulative hazard estimator. Simulation study is done for the data from exponential distribution with one hazard change-point. The simulated data generated without censoring and the data with right censoring are considered. As real data applications, the change-point estimates are computed for leukemia data and primary biliary cirrhosis data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.551-553
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• 2016

Since air compressor, as an essential equipment used in the factory and plant operations, accounts for around 20% of the total domestic electricity consumption, a real time sensor data monitoring based analysis for electricity consumption reduction is important. In particular, flow rates and pressures of these monitored variables has a direct correlation with the power consumption. This paper proposes a method to identify if the measurement error of the flow rate sensor comes from the sensor measurement limit through bivariate classification analysis of the flow rate and power using the EM (Expectation and Maximization) Algorithm and show how to enable more accurate analysis by the correlation between the flow rate and power on the right-censored data.