• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.341-352
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• 2003

Collecting data through experiment, the observers are an import source of measurement error and the inference on the measure of agreement, say kappa, is necessary. The models commonly used are complicated general mixture model, which have many nuisance parameters. Orthogonalization of parameters reduce the effect of nuisance parameter. Orthogonalization of estimating function gives the same effect as the parameter orthogonalization. In this study, the method for orthogonalization of estimating equation is studied and applied to the Beta-binomial model to examine the properties of the estimate of kappa. As a result, the likelihood function is insensitive to the change of the nuisance parameter and bias is smaller than the result of m.1.e. when kappa has extreme values

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.112-119
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• 2010

This paper defines the probability models for determining the disparity map given stereo images and derives the methods for solving the problem, which is proven to be equivalent to an energy-based stereo matching. Under the assumptions the difference between the pixel on the left image and the corresponding pixel on the right image and the difference between the disparities of the neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameter is proposed. Usually energy-based stereo matching methods are so sensitive to the parameter that it should be carefully determined. The proposed method alternates between estimating the parameter with the intermediate disparity map and estimating the disparity map with the estimated parameter, after computing it with random initial parameter. It is shown that the parameter estimated by the proposed method converges to the optimum and its performance can be improved significantly by adjusting the parameter and modifying the energy term.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.898-902
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• 1999

In this paper, we proposed an algorithm for estimating parameters of nonlinear continuous-discrete state-space system. This algorithm uses the conventional extended Kalman filter(EKF) for estimating state variables, and modifies the recursive prediction error method for parameter estimation of the nonlinear system. Simulation results for both linear and nonlinear measurements under the environment of process and measurement noises show a convincing performance of the proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.587-592
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• 2009

The most frequent use of the chi-square distribution is in the area of goodness-of-t of a distribution. The likelihood ratio test is a commonly used test statistic as the maximum likelihood estimate in statistical inferences. The recently revised versions of the likelihood ratio test statistics are used in estimating the parameter in the chi-square distribution. The estimates are compared with the commonly used method of moments and the maximum likelihood estimate.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.19-35
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• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.494-498
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• 1989

This paper describes an efficient time-varying parameter estimation algorithm by resetting the parameter and P matrix of the RLS algorithm. The described algorithm is useful for estimating both jump parameter and drifting parameter which vary quite rapidly.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.269-276
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• 2000

there are several methods available for estimating parameters in overdispersed binary response data with the litter effect. Simulations are performed to compare methods for estimating an overall mean and an overdispersion parameter using moments a maximum likelihood under a beta-binomial distribution a maximum quasi-likelihood and a maximum extended quasi-likelihood.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.103-108
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• 1989

PERT formulae for the mean and variance of activity time are near exact only over a short interval of the concentration parameter which is defined as the sum of the two shape parameters of the beta distribution. Aiming a better estimation of the mean and variance of activity time, we propose a method of subjectively estimating this concentration parameter via estimating the probability of completing the activity within a specified time interval.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.235-242
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• 1996

When the linear growth birth and death process observed at a set of equidistant time points, McNeil and Weiss (1997) present a method for simultaneously estimating the Malthusian parameter and the sum of the two parameters under wery restricted assumptions using a diffusion approximation. This article suggests a method, which does not require the restrictions given by Weiss, for estimating simultaneously the Malthusian parameter and the sum of the two parameters.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.89-99
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• 2006

This article deals with the problem of estimating parameters of Burr Type XII distribution, on the basis of a general progressive Type II censored sample using Bayesian viewpoints. The maximum likelihood estimator does not admit closed form but explicit sharp lower and upper bounds are provided. Assuming squared error loss and linex loss functions, Bayes estimators of the parameter k, the reliability function, and the failure rate function are obtained in closed form. Finally, a simulation study is also included.