• Title, Summary, Keyword: variable selection

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Variable Selection in Sliced Inverse Regression Using Generalized Eigenvalue Problem with Penalties

  • Park, Chong-Sun
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.215-227
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    • 2007
  • Variable selection algorithm for Sliced Inverse Regression using penalty function is proposed. We noted SIR models can be expressed as generalized eigenvalue decompositions and incorporated penalty functions on them. We found from small simulation that the HARD penalty function seems to be the best in preserving original directions compared with other well-known penalty functions. Also it turned out to be effective in forcing coefficient estimates zero for irrelevant predictors in regression analysis. Results from illustrative examples of simulated and real data sets will be provided.

Major Criteria for Channel Selection in Banking Transaction

  • Cho, Nam-Jae;Park, Ki-Ho
    • Journal of Information Technology Applications and Management
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    • v.16 no.1
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    • pp.169-183
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    • 2009
  • The purpose of this research, based on the Media Selection Theory, the Technology Acceptance Model, and the Social Influence Theory, is to investigate the influential factors that affect media selection in banking transactions. Analyses showed that for location sensitive bank windows and ATMs(automatic teller machines), defined as offline-based transaction channels, convenience was the variable affecting media selection. However, in the case of online media not related to location, (phone banking, internet banking, and mobile banking) reliability was the significant variable influencing use. The findings show that banking organizations may benefit from identifying traits of media affecting use, and should differentiate customer services for competitive advantage.

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Variable Selection Based on Mutual Information

  • Huh, Moon-Y.;Choi, Byong-Su
    • Communications for Statistical Applications and Methods
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    • v.16 no.1
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    • pp.143-155
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    • 2009
  • Best subset selection procedure based on mutual information (MI) between a set of explanatory variables and a dependent class variable is suggested. Derivation of multivariate MI is based on normal mixtures. Several types of normal mixtures are proposed. Also a best subset selection algorithm is proposed. Four real data sets are employed to demonstrate the efficiency of the proposals.

Variable Selection with Regression Trees

  • Chang, Young-Jae
    • The Korean Journal of Applied Statistics
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    • v.23 no.2
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    • pp.357-366
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    • 2010
  • Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.

Variable Selection with Nonconcave Penalty Function on Reduced-Rank Regression

  • Jung, Sang Yong;Park, Chongsun
    • Communications for Statistical Applications and Methods
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    • v.22 no.1
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    • pp.41-54
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    • 2015
  • In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

Efficient estimation and variable selection for partially linear single-index-coefficient regression models

  • Kim, Young-Ju
    • 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.

A convenient approach for penalty parameter selection in robust lasso regression

  • Kim, Jongyoung;Lee, Seokho
    • Communications for Statistical Applications and Methods
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    • v.24 no.6
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    • pp.651-662
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    • 2017
  • We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

Fast Frame Selection Method for Multi-Reference and Variable Block Motion Estimation (다중참조 및 가변블록 움직임 추정을 위한 고속 참조영상 선택 방법)

  • Kim, Sung-Dae;SunWoo, Myung-Hoon
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.6
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    • pp.1-8
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    • 2008
  • This paper introduces three efficient frame selection schemes to reduce the computation complexity for the multi-reference and variable block size Motion Estimation (ME). The proposed RSP (Reference Selection Pass) scheme can minimize the overhead of frame selection. The MFS (Modified Frame Selection) scheme can reduce the number of search points about 18% compared with existing schemes considering the motion of image during the reference frame selection process. In addition, the TPRFS (Two Pass Reference frame Selection) scheme can minimize the frame selection operation for the variable block size ME in H.264/AVC using the character of selected reference frame according to the block size. The simulation results show the proposed schemes can save up to 50% of the ME computation without degradation of image Qualify. Because the proposed schemes can be separated from the block matching process, they can be used with any existing single reference fast search algorithms.

Ordinal Variable Selection in Decision Trees (의사결정나무에서 순서형 분리변수 선택에 관한 연구)

  • Kim Hyun-Joong
    • The Korean Journal of Applied Statistics
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    • v.19 no.1
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    • pp.149-161
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    • 2006
  • The most important component in decision tree algorithm is the rule for split variable selection. Many earlier algorithms such as CART and C4.5 use greedy search algorithm for variable selection. Recently, many methods were developed to cope with the weakness of greedy search algorithm. Most algorithms have different selection criteria depending on the type of variables: continuous or nominal. However, ordinal type variables are usually treated as continuous ones. This approach did not cause any trouble for the methods using greedy search algorithm. However, it may cause problems for the newer algorithms because they use statistical methods valid for continuous or nominal types only. In this paper, we propose a ordinal variable selection method that uses Cramer-von Mises testing procedure. We performed comparisons among CART, C4.5, QUEST, CRUISE, and the new method. It was shown that the new method has a good variable selection power for ordinal type variables.

Robust varying coefficient model using L1 regularization

  • Hwang, Changha;Bae, Jongsik;Shim, Jooyong
    • 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.