• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.723-738
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• 2020

In this paper, we study a method to discriminate between trend stationary and difference stationary processes. Since a crucial ingredient of this discrimination is to determine the existence of unit root, we can use a unit root testing strategy. So, we introduce a discrimination based on unit root testing and propose the method using the adaptive lasso. Our Monte Carlo simulation experiments show that the adaptive lasso improves the discrimination accuracy when the process is trend stationary, but has lower accuracy than unit root strategy where the process is difference stationary.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.364-369
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• 2023

Time-delay estimation between two receivers is a technique that has been applied in a variety of fields, from underwater acoustics to room acoustics and robotics. There are two types of time delay estimation techniques: one that estimates the amount of time delay from the correlation between receivers, and the other that parametrically models the time delay between receivers and estimates the parameters by system recognition. The latter has the characteristic that only a small fraction of the system's parameters are directly related to the delay. This characteristic can be exploited to improve the accuracy of the estimation by methods such as Lasso regularization. However, in the case of Lasso regularization, the necessary information is lost. In this paper, we propose a method using Elastic Net that adds Ridge regularization to Lasso regularization to compensate for this. Comparing the proposed method with the conventional Generalized Cross Correlation (GCC) method and the method using Lasso regularization, we show that the estimation variance is very small even for white Gaussian signal sources and colored signal sources.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.21-37
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• 2021

There is a direct connection between linear discriminant analysis (LDA) and linear regression since the direction vector of the LDA can be obtained by the least square estimation. The connection motivates the penalized LDA when the model is high-dimensional where the number of predictive variables is larger than the sample size. In this paper, we study the penalized LDA for a class of penalties, called the moderately clipped LASSO (MCL), which interpolates between the least absolute shrinkage and selection operator (LASSO) and minimax concave penalty. We prove that the MCL penalized LDA correctly identifies the sparsity of the Bayes direction vector with probability tending to one, which is supported by better finite sample performance than LASSO based on concrete numerical studies.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.533-541
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• 2013

In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.

• Korean journal of applied entomology
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• v.10 no.1
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• pp.31-38
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• 1971

Tolerance test in plastic vat, pot and fold tests were carried out to investigate the selective herbicides for soybean culture in sandy loam. The soybean plants showed great tolerance against herbicides such as Tri-allate (Avadex-BW), Alachlor (Lasso), Butachlor (Machete), Propachlor (Ramrod), Nitrofen (TOK), MO, HE-314, Nitrofen/Dinoseb(TOK/DNBP), and Chlo.oxu.on (Tenoran), and the growth was normal even when each was treated with the herbicides up to 2ft3 times of the recommended concentrations. Soybean plants showed a slight tolerance against Prometryne (Gesagard), Propazine (Gegamil), Diuron (Karmex), Metabromuron (Patoran), Linuron (Lorox) and Swep when each was treated with herbicides up to 1-2 times of the recommended concentrations. Great injury or withering was noticed due to the high sensitivity of soybean to Simazine (CAT) and to Floumetron (Cotoran). In pot and field experiments with herbicides such as Butachlor (Machete), Alachlor (Lasso), Nitrofen (TOK), Kerb, Nitrofen/Dinoseb (TOK/DNBP), Swep, Linuron (Lorox), Simazine (CAT) and PCP, the following results were obtained: Great injuries were noticed with Simazine (CAT). Also, Linuron (Lorox) and Kerb showed a slight injury at early growth stage of soybean, Nitrofen (TOK) , Nitrofen/Dinoseb (TOK/DNEP), Alachlo. (Lasso), Butachlo. (Machete) and Swep had high selectivities for soybean and no injury was noticed. With respect to herbicidal effects there was a greatly significant difference between treated plots and non-treated plots with the exception of Simaaine (CAT) plot in field test. E. crusgalli and C. sanguinalis were tolerant against Simazine(CAT) and Linuron(Lorox). Cyperus and E. annuus were tolerant against Kerb. Great herbicidal effects on grasses were observed in Alachlor (Lasso) and Butachlor (Machete) plots. Among broad-leaf weeds, P. hydropiper and C. album were tolerant against Butachlor (Machete) and Alachlor (Lasso). When soybean was treated with the herbicides such as Alachlor (Lasso) (ai. 150g/10a), Butachlor (Machete) (ai. 300g/10a), Nitrofen (TOK) (ai. 250g/10a), Linuron (Lorox) (ai. 75g/10a) once after seeding, no additional wording was required till harvest.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6773-6777
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• 2015

We used to LASSO-Cox method for determining prognostic factors of male breast cancer survival and showed the superiority of this method compared to Cox proportional hazard model in low sample size setting. In order to identify and estimate exactly the relative hazard of the most important factors effective for the survival duration of male breast cancer, the LASSO-Cox method has been used. Our data includes the information of male breast cancer patients in Fars province, south of Iran, from 1989 to 2008. Cox proportional hazard and LASSO-Cox models were fitted for 20 classified variables. To reduce the impact of missing data, the multiple imputation method was used 20 times through the Markov chain Mont Carlo method and the results were combined with Rubin's rules. In 50 patients, the age at diagnosis was 59.6 (SD=12.8) years with a minimum of 34 and maximum of 84 years and the mean of survival time was 62 months. Three, 5 and 10 year survival were 92%, 77% and 26%, respectively. Using the LASSO-Cox method led to eliminating 8 low effect variables and also decreased the standard error by 2.5 to 7 times. The relative efficiency of LASSO-Cox method compared with the Cox proportional hazard method was calculated as 22.39. The19 years follow of male breast cancer patients show that the age, having a history of alcohol use, nipple discharge, laterality, histological grade and duration of symptoms were the most important variables that have played an effective role in the patient's survival. In such situations, estimating the coefficients by LASSO-Cox method will be more efficient than the Cox's proportional hazard method.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.147-157
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• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.561-575
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• 2014

In this paper, we propose a penalized likelihood method to detect local spatial clusters associated with disease. The key computational algorithm is based on genlasso by Tibshirani and Taylor (2011). The proposed method has two main advantages over Kulldorff's method which is popoular to detect local spatial clusters. First, it is not needed to specify a proper cluster size a priori. Second, any type of covariate can be incorporated and, it is possible to find local spatial clusters adjusted for some demographic variables. We illustrate our proposed method using tuberculosis data from Seoul.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.235-243
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• 2018

Many studies exist on the influence of one or few observations on estimators in a variety of statistical models under the "large n, small p" setup; however, diagnostic issues in the regression models have been rarely studied in a high dimensional setup. In the high dimensional data, the influence of observations is more serious because the sample size n is significantly less than the number variables p. Here, we investigate the influence of observations on the least absolute shrinkage and selection operator (LASSO) estimates, suggested by Tibshirani (Journal of the Royal Statistical Society, Series B, 73, 273-282, 1996), and the influence of observations on selected variables by the LASSO in the high dimensional setup. We also derived an analytic expression for the influence of the k observation on LASSO estimates in simple linear regression. Numerical studies based on artificial data and real data are done for illustration. Numerical results showed that the influence of observations on the LASSO estimates and the selected variables by the LASSO in the high dimensional setup is more severe than that in the usual "large n, small p" setup.