• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.615-624
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.7-12
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.15 no.2
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• pp.98-100
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• 2015

Short-chain acyl-CoA dehydrogenase (SCAD) deficiency is a rare mitochondrial fatty-acid oxidation disorder that is inherited as an autosomal recessive pattern. SCAD deficiency is caused by mutations in the ACADS gene (Acyl-CoA Dehydrogenase, Short-chain, OMIM #606885), which encodes SCAD, the mitochondrial enzyme that catalyzes the first reaction in the beta-oxidation of fatty acids four to six carbons in length. Here, we describe one Korean pediatric case of SCAD deficiency, which was diagnosed during newborn screening through tandem mass spectrometry. An increased concentration of butyrylcarnitine was detected on the newborn screening test, and the urine organic acid analysis showed increased urinary excretion of ethylmalonic acid. The patient has been asymptomatic and has shown normal growth and development by 8 months of age without any intervention during follow-up period.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.199-211
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• 2019

This paper is concerned with issues in the finite mixture of regression modeling as well as the simultaneous selection of the number of mixing components and relevant predictors. We propose a penalized likelihood method for both mixture components and regression coefficients that enable the simultaneous identification of significant variables and the determination of important mixture components in mixture of regression models. To avoid over-fitting and bias problems, we applied smoothly clipped absolute deviation (SCAD) penalties on the logarithm of component probabilities suggested by Huang et al. (Statistical Sinica, 27, 147-169, 2013) as well as several well-known penalty functions for coefficients in regression models. Simulation studies reveal that our method is satisfactory with well-known penalties such as SCAD, MCP, and adaptive lasso.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.655-662
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• 2012

Classification is an important research field in pattern recognition with high-dimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the non-convex penalty function, and the smoothly clipped absolute deviation(SCAD) suggested by Fan and Li (2001). We developed the algorithm for the multiclass SVM with the SCAD penalty function using the local quadratic approximation. For multiclass problems we compared the performance of the SVM with the $L_1$, $L_2$ penalty functions and the developed method.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.209-215
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• 2009

In regression problem, the SCAD estimator proposed by Fan and Li (2001), has many desirable property such as continuity, sparsity and unbiasedness. In this paper, we extend SCAD penalized regression framework to quantile regression and hence, we propose new SCAD penalized quantile estimator on high-dimensions and also present an efficient algorithm. From the simulation and real data set, the proposed estimator performs better than quantile regression estimator with $L_1$ norm.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.2 no.4
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• pp.5-16
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• 2003

We have analyzed the performance of optical matched filters in the fiber-optic code division multiple access (FO-CDMA) system based on optical parallel coupler access encoder (PCAE) and parallel coupler access decoder (PCAD) by experiment. In previous studies, the performance evaluation of the FO-CDMA system using SCAE and SCAD was not sufficiently accurate because they analyzed system performance only considering the first order signals. Since optical SCAE and SCAD intrinsically have high order signals of various patterns as the number of coupler increases, they change auto- and cross-correlation intensities. Thus, it is necessary to investigate properties of the PCAE and PCAD so that we may analyze the exact performance of system. In this paper, it is found that the peak to sidelobe ratio using the PCAE and PCAD increases as $\alpha$ (coupling coefficient) value increases. Also, we found that the proposed PCAE and PCAD are superior to SCAE and SCAD in performance improvement.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.1
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• pp.44-51
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• 2000

We have analyzed optical matched filters considering the third order signals in the optical code division multiple access (CDMA) system based on optical series coupler access encoder (SCAE) and series coupler access decoder (SCAD). In previous studies, the performance evaluation of the optical CDMA system using SCAE and SCAD was not sufficiently accurate because they analyzed system performance only considering the first order signals. Since optical SCAE and SCAD intrinsically have high order signals of various patterns as the number of coupler increases, they change auto- and cross-correlation intensities. Thus, it is necessary to investigate properties of the third order signals so that we may analyze the exact performance of system. In this paper, we mathematically interpret the optical signals up to the third order, and analyzed the effects of th third order signals on auto- and cross-correlation intensities. In result, as ${\alpha}$(coupling coefficient) value increases, the intensity of the third order signals increases. It is found that the peak to side-lobe ratio considering the third order signals is degraded by 3.75 dB at N(coupler number)=5 and ${\alpha}$=0.5. Also if threshold value in receiver is set by main-lobe peak of the first order signals, it is found that the number of users in an optical CDMA system is limited because the intensity peak of side-lobes is raised by the third order signals.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.441-457
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• 2024

Semicontinuous data are characterized by a mixture of a point probability mass at zero and a continuous distribution of positive values. This type of data is often modeled using a two-part model where the first part models the probability of dichotomous outcomes -zero or positive- and the second part models the distribution of positive values. Despite the two-part model's popularity, variable selection in this model has not been fully addressed, especially, in high dimensional data. The objective of this study is to investigate variable selection and prediction performance of penalized regression methods in two-part models. The performance of the selected techniques in the two-part model is evaluated via simulation studies. Our findings show that LASSO and ENET tend to select more predictors in the model than SCAD and MCP. Consequently, MCP and SCAD outperform LASSO and ENET for β-specificity, and LASSO and ENET perform better than MCP and SCAD with respect to the mean squared error. We find similar results when applying the penalized regression methods to the prediction of crime incidents using community-based data.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.15 no.2
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• pp.93-97
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• 2015

Short-chain acyl-CoA dehydrogenase (SCAD) deficiency is an autosomal recessive hereditary metabolic disorder of mitochondrial fatty acid beta-oxidation. Mutations in the ACADS gene cause short-chain acyl-CoA dehydrogenase deficiency, which is characterized by developmental delay, hypotonia, seizure, and hypoglycemia. Here, we describe one Korean pediatric case of SCAD deficiency, which was diagnosed during newborn screening by tandem mass spectrometry and confirmed by molecular analysis. The level of C4 was typically elevated 5.23 mg/dL (reference range <1.5 mg/dL). This patient had a homozygous mutation [c.1031A>G, p. E344G] in ACADS. Therefore, we present a case of SCAD deficiency in an otherwise healthy neonate and her subsequent development and growth over four years.