• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.523-542
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• 2007

In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.266-271
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• 2005

In this study, following works have been carried out : division of Nakdong River Basin into 25 sub basins, development of a technique to evaluate spatial distribution of rainfall and analysis of rainfall data of 169 stations, selection of control points, and selection of a hydrologic model(SSARR). The runoff analysis showed that the surface-subsurface separation and soil moisture index parameters are the most important two to the simulation result.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.459-464
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• 1999

The objective of this study is to analyze hydrologic recessiioon curve at the outlet of the ssangchi basin. For the development of recession equation, the initial discharge(Q0) and the recession parameters are estimated . It is shown that the accurate estimates of recession curve is easily obtained . The obtained parameters can be related to the basin characteristics, drainge area, and the total stream length so that they can be used for the development of the regional low flow estimation model.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.209-218
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• 2012

The method of generalized estimating equations (GEEs) provides consistent esti- mates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). In this paper we compare the estimators of parameters in GEE approach. We consider two aspects: coverage probabilites and efficiency. We adopted to ordinal responses th results derived from binary outcomes.

• International Journal of Reliability and Applications
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• v.8 no.2
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• pp.199-210
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• 2007

This paper presents estimations of the generalized Rayleigh distribution model based on grouped and censored data. The maximum likelihood method is used to derive point and asymptotic confidence estimates of the unknown parameters. The results obtained in this paper generalize some of those available in the literature. Finally, we test whether the current model fits a set of real data better than other models.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.307-308
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• 2007

This paper proposes a discriminative training algorithm for estimating hidden Markov model (HMM) parameters. The proposed algorithm estimates the Parameters by minimizing the p-norm of log-likelihood difference (PLD) between the utterance probability given the correct transcription and the most competitive transcription.

• International Journal of Reliability and Applications
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• v.11 no.1
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• pp.23-40
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• 2010

Recently, Zaindin and Sarhan (2009) introduced a new distribution named new generalized Weibull distribution. This paper deals with the problem of estimating the parameters of this distribution in the case where the data is grouped and censored. We use both the maximum likelihood and Bayes techniques. The results obtained are illustrated on a set of real data.

• Journal of the Korea Society for Simulation
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• v.10 no.3
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• pp.31-46
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• 2001

Repeated measurements on units under different conditions are common in biological and biomedical studies. In a number of growth and pharmacokinetic studies, the relationship between the response and the covariates is assumed to be nonlinear in some unknown parameters and the form remains the same for all units. Nonlinear random coefficient models are used to analyze such repeated measurement data. Extended least squares methods are proposed in the literature for estimating the parameters of the model. However, neither objective function has closed form expression in practice. This paper proposes Monte Carlo methods to estimate the objective functions and the corresponding estimators. A simulation study that compare various methods is included.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.113-116
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• 2006

In order to fully utilize the inherent precision that GPS observables could offer, accurate estimation of dynamic and measurement parameters is vital. Among these parameters some are indispensable in virtually every form of GPS processing, while some are limitedly relevant to a particular application. In the context of time transfer by GPS, the transmission-related errors such as ionospheric and tropospheric delays, and the integer ambiguity of the carrier phase observables belong to the former, the atomic clock parameters and data batch-related parameters to the latter. Obviously the atomic clock parameters are of prime importance in GPS time transfer. In this study some of important parameters in conducting time transfer experiments by use of GPS were characterized and their effects on time transfer performance were investigated in detail

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.