• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.809-824
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• 2002

we propose two methods which separate the variable selection step and the split-point selection step. We call these two algorithms as CHITES method and F&CHITES method. They adapted some of the best characteristics of CART, CHAID, and QUEST. In the first step the variable, which is most significant to predict the target class values, is selected. In the second step, the exhaustive search method is applied to find the splitting point based on the selected variable in the first step. We compared the proposed methods, CART, and QUEST in terms of variable selection bias and power, error rates, and training times. The proposed methods are not only unbiased in the null case, but also powerful for selecting correct variables in non-null cases.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.47-55
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• 2019

It is important to identify informative variables in high dimensional data analysis; however, it becomes a challenging task when covariates are contaminated by measurement error due to the bias induced by measurement error. In this article, we present a two-step approach for variable selection in the presence of measurement error. In the first step, we directly select important variables from the contaminated covariates as if there is no measurement error. We then apply, in the following step, orthogonal regression to obtain the unbiased estimates of regression coefficients identified in the previous step. In addition, we propose a modification of the two-step approach to further enhance the variable selection performance. Various simulation studies demonstrate the promising performance of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.121-127
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• 2004

This paper presents a new vertex selection scheme for shape approximation. In the proposed method, final vertex points are determined by "two-step procedure". In the first step, initial vertices are simply selected on the contour, which constitute a subset of the original contour, using conventional methods such as an iterated refinement method (IRM) or a progressive vertex selection (PVS) method In the second step, a vertex adjustment Process is incorporated to generate final vertices which are no more confined to the contour and optimal in the view of the given distortion measure. For the optimality of the final vertices, the dynamic programming (DP)-based solution for the adjustment of vertices is proposed. There are two main contributions of this work First, we show that DP can be successfully applied to vertex adjustment. Second, by using DP, the global optimality in the vertex selection can be achieved without iterative processes. Experimental results are presented to show the superiority of our method over the traditional methods.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.347-353
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• 2022

Using the Heckman two-step procedure, this study investigates the relationship between management accounting implementation and firm effectiveness. The research data for this study was acquired from 450 publicly traded companies in Vietnam; however, the final sample only includes 304 responses containing useful information. The reliability analysis was used to evaluate the acquired data to examine the qualities of constructs and the dimensions that make them up. Then, the Heckman two-step technique was performed to analyze the causal connection from the acceptance of management accounting to firm effectiveness allowing for the effect of environmental uncertainty and organizational characteristics on the likelihood of adopting management accounting. The empirical findings show that management accounting acceptance determines firm effectiveness; however, the research model on the relationship between management accounting adoption and firm effectiveness has a sample selection bias. The main conclusions of this study are that there is a difference in the effects of management accounting adoption on business effectiveness when sample selection bias is not taken into consideration. When potential sample selection bias is taken into account by integrating environmental uncertainty and organizational characteristics in the research model, the effect of adopting management accounting on company effectiveness becomes minor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4180-4196
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• 2017

In this paper, we analyze average capacity of an amplify-and-forward (AF) cooperative communication system model in multi-relay multiuser networks. In contrast to conventional cooperative networks, relays in the considered network have no embedded energy supply. They need to rely on the energy harvested from the signals broadcasted by the source for their cooperative information transmission. Based on this structure, a two-step selection scheme is proposed considering both channel state information (CSI) and battery status of relays. Assuming each relay has infinite or finite energy storage for accumulating the energy, we use the infinite or finite Markov chain to capture the evolution of relay batteries and certain simplified assumptions to reduce computational complexity of the Markov chain analysis. The approximate closed-form expressions for the average capacity of the proposed scheme are derived. All theoretical results are validated by numerical simulations. The impacts of the system parameters, such as relay or user number, energy harvesting threshold and battery size, on the capacity performance are extensively investigated. Results show that although the performance of our scheme is inferior to the optimal joint selection scheme, it is still a practical scheme because its complexity is much lower than that of the optimal scheme.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.505-508
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• 2003

The current paper proposes a new vertex selection scheme for polygon-based contour ceding. To efficiently characterize the shape of an object, we incorporate the curvature information in addition to the conventional maximum distance criterion in vertex selection process. The proposed method consists of “two-step procedure.” At first, contour pixels of high curvature value are selected as key vertices based on the curvature scale space (CSS), thereby dividing an overall contour into several contour-segments. Each segment is considered as an open contour whose end points are two consecutive key vertices and is processed independently. In the second step, vertices for each contour segment are selected using progressive vertex selection (PVS) method in order to obtain minimum number of vertices under the given maximum distance criterion ( $D_{MAX}$). Experimental results are presented to compare the approximation performances of the proposed and conventional methods.s.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10C
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• pp.614-620
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• 2011

RSA, the most commonly used public-key cryptosystem, is based on the difficulty of factoring very large integers. The fastest known factoring algorithm is the Number Field Sieve(NFS). NFS first chooses two polynomials having common root modulo N and consists of the following four major steps; 1. Polynomial Selection 2. Sieving 3. Matrix 4. Square Root, of which the most time consuming step is the Sieving step. However, in recent years, the importance of the Polynomial Selection step has been studied widely, because one can save a lot of time and memory in sieving and matrix step if one chooses optimal polynomial for NFS. One of the ideal ways of choosing sieving polynomial is to choose two polynomials with same degree. Montgomery proposed the method of selecting two (nonlinear) quadratic sieving polynomials. We proposed two cubic polynomials using 5-term geometric progression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4375-4388
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• 2018

To solve the problem of transmission errors in stereoscopic images, this paper proposes a novel error concealment (EC) method using superpixel segmentation and adaptive disparity selection (SSADS). Our algorithm consists of two steps. The first step is disparity estimation for each pixel in a reference image. In this step, the numbers of superpixel segmentation labels of stereoscopic images are used as a new constraint for disparity matching to reduce the effect of mismatching. The second step is disparity selection for a lost block. In this step, a strategy based on boundary smoothness is proposed to adaptively select the optimal disparity which is used for error concealment. Experimental results demonstrate that compared with other methods, the proposed method has significant advantages in both objective and subjective quality assessment.

• Nutrition Research and Practice
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• v.8 no.5
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• pp.595-601
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• 2014

BACKGROUND/OBJECTIVES: Educational interventions targeted food selection perception, knowledge, attitude, and behavior. Education regarding irradiated food was intended to change food selection behavior specific to it. SUBJECTS AND METHODS: There were 43 elementary students (35.0%), 45 middle school students (36.6%), and 35 high school students (28.5%). The first step was research design. Educational targets were selected and informed consent was obtained in step two. An initial survey was conducted as step three. Step four was a 45 minute-long theoretical educational intervention. Step five concluded with a survey and experiment on food selection behavior. RESULTS: As a result of conducting a 45 minute-long education on the principles, actual state of usage, and pros and cons of irradiated food for elementary, middle, and high-school students in Korea, perception, knowledge, attitude, and behavior regarding the irradiated food was significantly higher after the education than before the education (P < 0.000). CONCLUSIONS: The behavior of irradiated food selection shows high correlation with all variables of perception, knowledge, and attitude, and it is necessary to provide information of each level of change in perception, knowledge, and attitude in order to derive proper behavior change, which is the ultimate goal of the education.

• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.