• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.397-408
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• 2018

Various methods for accurate parameter estimation have been developed in a sample survey and it is also common to use a ratio estimator or the regression estimator using auxiliary information. The ratio-type estimator has been used in many recent studies and is known to improve the accuracy of estimation by adjusting the ratio estimator. However, various studies are under way to solve it since the ratio-type estimator is biased. In this study, we propose a generalized ratio-type estimator with a new parameter added to the ratio-type estimator to remove the bias. We suggested a method to apply this result to the parameter estimation under the error assumption of heteroscedasticity. Through simulation, we confirmed that the suggested generalized ratio-type estimator gives good results compared to conventional ratio-type estimators.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.811-821
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• 2007

The intensity-duration-frequency (IDF) curves by Talbot, Sherman and Japanese type formulas are widely used in South Korea since the parameters are easily estimated. However, these IDF curves' accuracies are relatively worse than those of the IDF curves developed by Lee et al. (1993) and Heo et al. (1999), and different parameters for the given return periods should be computed. In this study, parameter estimation method for the IDF curve by Heo et al. (1999) is suggested using genetic algorithm (GA). Quantiles computed by at-site frequency analysis using the rainfall data of 22 rainfall gauges operated by Korea Meteorological Administration are employed to estimate the parameters of IDF curves and minimizing root mean squared error (RMSE) and relative RMSE (RRMSE) of observed and computed quantiles are used as objective functions of GA. The comparison of parameter estimation methods between the empirical regression analysis and the suggested method show that the IDF curve in which the parameters are estimated by GA using RRMSE as an objective function is superior to the IDF curves using RMSE.

• Explosives and Blasting
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• v.41 no.3
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• pp.73-92
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• 2023

With the increase of risk of gas explosion, various methods for indirectly estimating the explosion paramaters, which are required for the prediction of gas explosion scale and impact. In this study, the characteristics of the most frequently used methods such as TNT equivalent method, TNO multi-energy method, and BST method and the processes for determining the parameters of the methods were compared. In the case of TNT equivalent method, an adequate selection of the efficiency factor for various conditions such as the type of vapor cloud explosion and explosion material is needed. There is no objective guidelines for the selection of class number in TNO multi-energy method and it is not possible to estimate negative overpressure. It was found that there were some mistakes in the reported parameter values and suggested corrected values. BST method provides more detailed guidelines for the estimation of the explosion parameters including negative overpressure, but the graphs used in this methods are not clear. In order to overcome the problem, the graphs were redrawn. A more convenient estimation of explosion parameters with the numerical expression of the redrawn graphs will be available in the future.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.21-29
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• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.283-292
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• 2008

Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.

• Smart Structures and Systems
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• v.14 no.5
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• pp.831-845
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• 2014

This paper proposed a discrete wavelet transform based method for time-varying physical parameter identification of shear type structures. The time-varying physical parameters are dispersed and expanded at multi-scale as profile and detail signal using discrete wavelet basis. To reduce the number of unknown quantity, the wavelet coefficients that reflect the detail signal are ignored by setting as zero value. Consequently, the time-varying parameter can be approximately estimated only using the scale coefficients that reflect the profile signal, and the identification task is transformed to an equivalent time-invariant scale coefficient estimation. The time-invariant scale coefficients can be simply estimated using regular least-squares methods, and then the original time-varying physical parameters can be reconstructed by using the identified time-invariant scale coefficients. To reduce the influence of the ill-posed problem of equation resolving caused by noise, the Tikhonov regularization method instead of regular least-squares method is used in the paper to estimate the scale coefficients. A two-story shear type frame structure with time-varying stiffness and damping are simulated to validate the effectiveness and accuracy of the proposed method. It is demonstrated that the identified time-varying stiffness is with a good accuracy, while the identified damping is sensitive to noise.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.27-30
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• 1999

A spatio-temporal vector channel model is introduced for the position location (PL) estimation problem for CDMA cellular system environment. Two common ways for the PL make use of the AOA (Angle Of Arrival) and TDOA (Time Difference Of Arrival) from a subscriber to the multiple sensors (base stations). In this paper, we applied the derived vector channel to simulate the multipath channel for the angle of the signal arrival in CDMA systems. Cross-correlation method is a good candidate among other direction finding algorithms available in literature, especially in wideband modulation as in the CDMA system. The PL estimation errors are evaluated for different channels, which are obtained as a parameter of scattering radius of the suggested model. We noted that the number of sensors (base-stations) are related to the PL errors in favor of the available data.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.147-160
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• 2014

In this paper, we introduce the exponentiated Weibull-geometric (EWG) distribution which generalizes two-parameter exponentiated Weibull (EW) distribution introduced by Mudholkar et al. (1995). This proposed distribution is obtained by compounding the exponentiated Weibull with geometric distribution. We derive its cumulative distribution function (CDF), hazard function and the density of the order statistics and calculate expressions for its moments and the moments of the order statistics. The hazard function of the EWG distribution can be decreasing, increasing or bathtub-shaped among others. Also, we give expressions for the Renyi and Shannon entropies. The maximum likelihood estimation is obtained by using EM-algorithm (Dempster et al., 1977; McLachlan and Krishnan, 1997). We can obtain the Bayesian estimation by using Gibbs sampler with Metropolis-Hastings algorithm. Also, we give application with real data set to show the flexibility of the EWG distribution. Finally, summary and discussion are mentioned.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.31-52
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• 2008

In this paper generalized version of the geometric distribution is introduced. This distribution can be considered as a two-parameter generalization of the discrete geometric distribution. The main statistical and reliability properties of this distribution are discussed. Two methods of estimation, namely maximum likelihood method and the method of moments are used to estimate the parameters of this distribution. Simulation is utilized to calculate these estimates and to study some of their properties. Also, asymptotic confidence limits are established for the maximum likelihood estimates. Finally, the appropriateness of this new distribution for a set of real data, compared with the geometric distribution, is shown by using the likelihood ratio test and the Kolmogorove-Smirnove test.