• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.721-733
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• 2022

In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.88-93
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• 2024

The main problem with existing edge detection techniques is that they have many limitations in detecting edges for complex and diverse images that exist in the real world. This is because only edges of a defined shape are discovered based on an accurate definition of the edge. One of the methods to solve this problem is the cost minimization method. In the cost minimization method, cost elements and cost functions are defined and used. The cost function calculates the cost for the candidate edge model generated according to the candidate edge generation strategy, and if the cost is found to be satisfactory, the candidate edge model becomes the edge for the image. In this study, we proposed an enhanced candidate edge generation strategy to discover edges for more diverse types of images in order to improve the shortcoming of the cost minimization method, which is that it only discovers edges of a defined type. As a result, improved edge detection results were confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1377-1389
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• 2016

This paper presents a target model update scheme for the mean-shift tracking with background weighted histogram. In the scheme, the target candidate histogram is corrected by considering the back-projection weight of each pixel in the kernel after the best target candidate in the current frame image is chosen. In each frame, the target model is updated by the weighted average of the current target model and the corrected target candidate. We compared our target model update scheme with the previous ones by applying several test sequences. The experimental results showed that the object tracking accuracy was greatly improved by using the proposed scheme.

• 대한안전경영과학회지
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• 제15권1호
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• pp.303-315
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• 2013

The purpose of this study is to explore the mediator effect of the presidential candidate brand affiliated to the certain party toward enhancing the party brand equity and the presidential candidate brand value. In detail, firstly, the study attempts to identify the determinants of the party brand equity. Secondly, we clarify the factors of presidential candidate brand value. Finally, the paper testifies the proposed mediator model of the presidential candidate brand with respect to the impact of the belonging party brand equity in voting intention. Results show that the mediator effect of candidate brand exists between the affiliated party and the voting intention. In voting intention, the perceived quality of the party brand equity significantly influences on the candidate brand. Brand loyalty and Brand association of the party brand equity don't impact on the candidate brand significantly. In addition, the result proposes the all components of candidate brand value have significant impacts on voting intention. This paper is an initial attempts to regard the political parties and affiliated candidates in terms of brand marketing as the party brand equity and the candidate brand value respectively. With respect to better enhancing an approval rating, the study is proposed to the parties and candidates to-do list and direction of brand equity management.

• 대한기계학회논문집A
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• 제31권1호
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• pp.55-61
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• 2007

Recently simulation model becomes an essential tool for analysis and design of a system but it is often expensive and time consuming as it becomes complicate to achieve reliable results. Therefore, high-fidelity simulation model needs to be replaced by an approximate model, the so-called metamodel. Metamodeling techniques include 3 components of sampling, metamodel and validation. Cross-validation approach has been proposed to provide sequnatially new sample point based on cross-validation error but it is very expensive because cross-validation must be evaluated at each stage. To enhance the cross-validation of metamodel, sequential sampling method using candidate points and representative cross-validation is proposed in this paper. The candidate and representative cross-validation approach of sequential sampling is illustrated for two-dimensional domain. To verify the performance of the suggested sampling technique, we compare the accuracy of the metamodels for various mathematical functions with that obtained by conventional sequential sampling strategies such as maximum distance, mean squared error, and maximum entropy sequential samplings. Through this research we team that the proposed approach is computationally inexpensive and provides good prediction performance.

• Korean Journal of Hydrosciences
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• 제3권
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• pp.81-96
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• 1992

This study represents the methodology for feasibility analysis of small hydro power SHP plant. Cumulative density function of Weibull distribution and Thiessen method were adopted to decide flow duration curve at SHP candidate site. The perfomance prediction model and construction cost estimation model for tunnel-type SHP plant were developed. Eight tunnel -type SHP candidate sites existing on Han-river were selected and surveyed for actual site reconnaissance. The performance characteristics and economical feasibility for these sites were analyzed by using developed models. As a result, it was found that the optimum design flowrate with the lowest unit generation cost for tunel-type SHP candidate site were the flowrate concerming with between 20% and 30% of time ratio on the flow duration curve. Additionally, primary design specifications such as design flowrate, effective head, capacity, annual averageload factor, annual electricity production were estimated and discussed for eight surveyed SHP candidate sites.

• 한국경영과학회지
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• 제28권2호
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• pp.129-138
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• 2003

The traveling salesman problem (TSP) is an NP-hard problem. As the number of nodes increases, it takes a lot of time to find an optimal solution. Instead of considering all arcs, if we select and consider only some arcs more likely to be included in an optimal solution, we can find efficiently an optimal solution. Arc candidate set is a group of some good arcs. For the Lack of study in the asymmetric TSP. it needs to research arc candidate set for the asymmetric TSP systematically. In this paper, we suggest a regression function determining arc candidate set for the asymmetric TSP. We established the function based on 2100 experiments, and we proved the goodness of fit for the model through various 787problems. The result showed that the optimal solutions obtained from our arc candidate set are equal to the ones of original problems. We expect that this function would be very useful to reduce the complexity of TSP.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.589-601
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• 2001

Probabilities of overfilling for model selection criteria are derived for several different situations. First, one candidate model with one extra variable is compared to the current model. This is expanded to m candidate models. We show that these comparisons are not independent and discuss ovefitting probabilities. Correlation between two F-tests is derived. Finally, probabilities are computed using the dependent F distributions and F distributions based on order statistics of independent Chi-squares.

• Journal of the Korean Statistical Society
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• 제35권3호
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• pp.261-280
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• 2006

We have developed a new model selection criteria, the partial intrinsic Bayes factor, which is designed for cases when we select a model among a small number of candidate models. For example, we can choose only a few candidate models after exploring scatter plots. By simulation study, we have showed that PIBF performs better than AIC, BIC and GCV.