• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.261-274
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• 2014

In this paper we develop Bayes and empirical Bayes estimators of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation under the balanced loss function. We compare the performance of the optimal Bayes estimator with ones of the classical sample mean and the usual Bayes estimator under the squared error loss with respect to the posterior expected losses, risks and Bayes risks when the underlying distribution is normal as well as when they are binomial and Poisson.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.685-696
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• 2014

In this paper, we develop Bayesian inference of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation in the presence of auxiliary information under the balanced loss function. We compare the performance of the optimal Bayes estimator under the balanced loss function with ones of the classical ratio estimator and the usual Bayes estimator in terms of the posterior expected losses, risks and Bayes risks.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.1
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• pp.53-60
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• 2015

Purpose : The purpose of this study is to evaluate the usefulness of a 3D printed phantom for in-vivo dosimetry of a prostate cancer patient. Materials and Methods : The phantom is produced to equally describe prostate and rectum based on a 3D volume contour of an actual prostate cancer patient who is treated in Asan Medical Center by using a 3D printer (3D EDISON+, Lokit, Korea). CT(Computed tomography) images of phantom are aquired by computed tomography (Lightspeed CT, GE, USA). By using treatment planning system (Eclipse version 10.0, Varian, USA), treatment planning is established after volume of a prostate cancer patient is compared with volume of the phantom. MOSFET(Metal OXIDE Silicon Field Effect Transistor) is estimated to identify precision and is located in 4 measuring points (bladder, prostate, rectal anterior wall and rectal posterior wall) to analyzed treatment planning and measured value. Results : Prostate volume and rectum volume of prostate cancer patient represent 30.61 cc and 51.19 cc respectively. In case of a phantom, prostate volume and rectum volume represent 31.12 cc and 53.52 cc respectively. A variation of volume between a prostate cancer patient and a phantom is less than 3%. Precision of MOSFET represents less than 3%. It indicates linearity and correlation coefficient indicates from 0.99 ~ 1.00 depending on dose variation. Each accuracy of bladder, prostate, rectal anterior wall and rectal posterior wall represent 1.4%, 2.6%, 3.7% and 1.5% respectively. In- vivo dosimetry represents entirely less than 5% considering precision of MOSFET. Conclusion : By using a 3D printer, possibility of phantom production based on prostate is verified precision within 3%. effectiveness of In-vivo dosimetry is confirmed from a phantom which is produced by a 3D printer. In-vivo dosimetry is evaluated entirely less than 5% considering precision of MOSFET. Therefore, This study is confirmed the usefulness of a 3D printed phantom for in-vivo dosimetry of a prostate cancer patient. It is necessary to additional phantom production by a 3D printer and In-vivo dosimetry for other organs of patient.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.