• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.461-471
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• 2017

In this paper we propose a feature selection method identifying important features in the semivarying coefficient model. One important issue in semivarying coefficient model is how to estimate the parametric and nonparametric components. Another issue is how to identify important features in the varying and the constant effects. We propose a feature selection method able to address this issue using generalized cross validation functions of the varying coefficient least squares support vector regression (LS-SVR) and the linear LS-SVR. Numerical studies indicate that the proposed method is quite effective in identifying important features in the varying and the constant effects in the semivarying coefficient model.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1059-1066
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• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.1
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• pp.36-42
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• 2016

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to $2^*SNP$ effect.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.345-350
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• 1999

The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.17-23
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• 2012

Recently, for spectral efficiency and power saving, an incremental best relay selection of a cooperative diversity scheme is proposed. However during the best relay selection process, there may exist a delay between channel estimation and actual data transmission. Consequently, this delay causes outdated channel state information (CSI). We analytically derive the effect of the outdated CSI to an incremental best relay selection diversity scheme. It is noted that the system performance deteriorates with decreasing the value of the correlation coefficient sensitively. When the correlation coefficient reduces from 1 to 0.9, the most performance degradation is denoted. However, the performance degradation is diminished with decreasing the correlation coefficient.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1564-1569
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• 2009

Project managers play a key role in cost, time, and quality of a project. Selection of an appropriate project manager, therefore, is considered as one of the most important decisions in any construction project. It should be noted that most important decision makings are carried out by the project manager throughout the project. Traditionally, project manager selection in construction companies in Iran is through organizing an interview with candidates and selecting the most appropriate choice in accordance with the capabilities, potentials and individual specifications coupled with the requirements of the project. In the same direction, organizing interview on selection of appropriate candidate is usually carried out by senior managers of companies. Determination of the most important criteria for selection of project managers and also identification of significance coefficient of each criterion can highly help senior managers of companies to make sound selection decisions. In this paper, a numerical model has been considered for determination of significance of each criterion, details of which are submitted for selection of project manager in Iranian petrochemical, oil and gas sector companies. For this reason, all criteria- considered by senior managers of the companies under study- are first determined. Then, information obtained through 38 interviews, conducted by senior managers of the mentioned companies while selecting project manager, is analyzed. Significant coefficient of each criterion is calculated through the accumulated data using fuzzy curves method.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.8
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• pp.856-862
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• 2015

Tidal current energy is an important alternative energy resource among the various ocean energy resources available. The tidal currents in the South-Western sea of Korea can be utilized for the development of tidal current power generation. Tidal power generation can be beneficial for many fishing nurseries and nearby islands in the southwest region of Korea. Moreover, tidal power generation is necessary for promoting energy self-sufficient islands. As tidal currents are always available, power generation is predictable; thus, tidal power is a reliable renewable energy resource. The selection of an appropriate hydrofoil is important for designing a tidal current turbine. This study concentrates on the selection and numerical analysis of four different hydrofoils (MNU26, NACA63421, DU91_W2_250, and DU93_W_210LM). Blade element momentum theory is used for configuring the design of a 50 W class turbine rotor blade. The optimized blade geometry is used for computational fluid dynamics (CFD) analysis with hexahedral numerical grids. Among the four blades, NACA63421 blade showed the maximum power coefficient of 0.45 at a tip speed ratio of 6. CFD analysis is used to investigate the power coefficient, pressure coefficient, and streamline distribution of a 50 W class horizontal axis tidal current turbine for different hydrofoils.

• Journal of Ginseng Research
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• v.19 no.2
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• pp.165-170
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• 1995

This study was carried out to investigate the correlation of agronomic characters, their path coefficients in 2, 3 and 4-year old ginseng plants, and to provide a useful information for ginseng breeding. Correlation coefficients between stem fen변h, number of leaves and number of Iraflets in 2-year age, and stem diameter and leaf length in 3-year age showed highly significant correlation with number of fruits and root weight in 4-year age. The path coefficient analysis indicated that stem length and number of leaflets might give indirect effects on root weight regardless of plant age. On the other hand, stem length and number of leaflets in 2-year age and, stem diameter and leaf length in 3-year age showed direct effects on root weight in 4-year old ginseng. These results may be used for selection of high-yielding ginseng plants. Key words Selection information, correlation and path coefficient analysis.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.901-908
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• 2010

A geometrical description method is proposed to represent the process of the forward selection and backward elimination methods among many variable selection methods for multiple regression models. This graphical method shows the process of the forward selection and backward elimination on the first and second quadrants, respectively, of half circle with a unit radius. At each step, the SSR is represented by the norm of vector and the extra SSR or partial determinant coefficient is represented by the angle between two vectors. Some lines are dotted when the partial F test results are statistically significant, so that statistical analysis could be explored. This geometrical description can be obtained the final regression models based on the forward selection and backward elimination methods. And the goodness-of-fit for the model could be explored.