• Title, Summary, Keyword: variable selection

Search Result 754, Processing Time 0.047 seconds

Variable selection with quantile regression tree (분위수 회귀나무를 이용한 변수선택 방법 연구)

  • Chang, Youngjae
    • The Korean Journal of Applied Statistics
    • /
    • v.29 no.6
    • /
    • pp.1095-1106
    • /
    • 2016
  • The quantile regression method proposed by Koenker et al. (1978) focuses on conditional quantiles given by independent variables, and analyzes the relationship between response variable and independent variables at the given quantile. Considering the linear programming used for the estimation of quantile regression coefficients, the model fitting job might be difficult when large data are introduced for analysis. Therefore, dimension reduction (or variable selection) could be a good solution for the quantile regression of large data sets. Regression tree methods are applied to a variable selection for quantile regression in this paper. Real data of Korea Baseball Organization (KBO) players are analyzed following the variable selection approach based on the regression tree. Analysis result shows that a few important variables are selected, which are also meaningful for the given quantiles of salary data of the baseball players.

Variable Selection in Clustering by Recursive Fit of Normal Distribution-based Salient Mixture Model (정규분포기반 두각 혼합모형의 순환적 적합을 이용한 군집분석에서의 변수선택)

  • Kim, Seung-Gu
    • The Korean Journal of Applied Statistics
    • /
    • v.26 no.5
    • /
    • pp.821-834
    • /
    • 2013
  • Law et al. (2004) proposed a normal distribution based salient mixture model for variable selection in clustering. However, this model has substantial problems such as the unidentifiability of components an the inaccurate selection of informative variables in the case of a small cluster size. We propose an alternative method to overcome problems and demonstrate a good performance through experiments on simulated data and real data.

Two-Stage Penalized Composite Quantile Regression with Grouped Variables

  • Bang, Sungwan;Jhun, Myoungshic
    • Communications for Statistical Applications and Methods
    • /
    • v.20 no.4
    • /
    • pp.259-270
    • /
    • 2013
  • This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

Penalized variable selection for accelerated failure time models

  • Park, Eunyoung;Ha, Il Do
    • Communications for Statistical Applications and Methods
    • /
    • v.25 no.6
    • /
    • pp.591-604
    • /
    • 2018
  • The accelerated failure time (AFT) model is a linear model under the log-transformation of survival time that has been introduced as a useful alternative to the proportional hazards (PH) model. In this paper we propose variable-selection procedures of fixed effects in a parametric AFT model using penalized likelihood approaches. We use three popular penalty functions, least absolute shrinkage and selection operator (LASSO), adaptive LASSO and smoothly clipped absolute deviation (SCAD). With these procedures we can select important variables and estimate the fixed effects at the same time. The performance of the proposed method is evaluated using simulation studies, including the investigation of impact of misspecifying the assumed distribution. The proposed method is illustrated with a primary biliary cirrhosis (PBC) data set.

Penalized rank regression estimator with the smoothly clipped absolute deviation function

  • Park, Jong-Tae;Jung, Kang-Mo
    • Communications for Statistical Applications and Methods
    • /
    • v.24 no.6
    • /
    • pp.673-683
    • /
    • 2017
  • The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

Validation Comparison of Credit Rating Models Using Box-Cox Transformation

  • Hong, Chong-Sun;Choi, Jeong-Min
    • Journal of the Korean Data and Information Science Society
    • /
    • v.19 no.3
    • /
    • pp.789-800
    • /
    • 2008
  • Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

  • PDF

Variable selection in censored kernel regression

  • Choi, Kook-Lyeol;Shim, Jooyong
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.1
    • /
    • pp.201-209
    • /
    • 2013
  • For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.

A Study on Auxiliary Variable Selection in Unit Nonresponse Calibration (단위 무응답 보정에서 보조변수의 선택에 관한 연구)

  • 손창균;홍기학;이기성
    • The Korean Journal of Applied Statistics
    • /
    • v.16 no.1
    • /
    • pp.33-44
    • /
    • 2003
  • Typically, it should be use auxiliary variable for calibrating the survey nonreponse in census or sampling survey. Where, if the dimension of auxiliary information is large, then it nay be spend a lot of computing time, and difficult to handle data set. Also because the variance estimator depends on the dimension of auxiliary variables, the variance estimator becomes underestimator. To deal with this problem, we propose the variable selection methods for calibration estimation procedure in unit nonreponse situation and we compare the efficiency by simulation study.

Fast Decoder Algorithm Using Hybrid Beam Search and Variable Flooring for Large Vocabulary Speech Recognition (대용량 음성인식을 위한 하이브리드 빔 탐색 방법과 가변 플로링 기법을 이용한 고속 디코더 알고리듬 연구)

  • Kim, Yong-Min;Kim, Jin-Young;Kim, Dong-Hwa;Kwon, Oh-Il
    • Speech Sciences
    • /
    • v.8 no.4
    • /
    • pp.17-33
    • /
    • 2001
  • In this paper, we implement the large variable vocabulary speech recognition system, which is characterized by no additional pre-training process and no limitation of recognized word list. We have designed the system in order to achieve the high recognition rate using the decision tree based state tying algorithm and in order to reduce the processing time using the gaussian selection based variable flooring algorithm, the limitation algorithm of the number of nodes and ENNS algorithm. The gaussian selection based variable flooring algorithm shows that it can reduce the total processing time by more than half of the recognition time, but it brings about the reduction of recognition rate. In other words, there is a trade off between the recognition rate and the processing time. The limitation algorithm of the number of nodes shows the best performance when the number of gaussian mixtures is a three. Both of the off-line and on-line experiments show the same performance. In our experiments, there are some differences of the recognition rate and the average recognition time according to the distinction of genders, speakers, and the number of vocabulary.

  • PDF

Effect of Different Variable Selection and Estimation Methods on Performance of Fault Diagnosis (이상진단 성능에 미치는 변수선택과 추정방법의 영향)

  • Cho, Hyun-Woo
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.20 no.9
    • /
    • pp.551-557
    • /
    • 2019
  • Diagnosis of abnormal faults is essential for producing high quality products. The role of real-time diagnosis is quite increasing in the batch processes of producing high value-added products such as semiconductors, pharmaceuticals, and so forth. In this study, we evaluate the effect of variable selection and future-value estimation techniques on the performance of the diagnosis system, which is based on nonlinear classification and measurement data. The diagnostic performance can be improved by selecting only the variables that are important and have high contribution for diagnosis. Thus, the diagnostic performance of several variable selection techniques is compared and evaluated. In addition, missing data of a new batch, called future observations, should be estimated because the full data of a new batch is not available before the end of the cycle. In this work the use of different estimation techniques is analyzed. A case study on the polyvinyl chloride batch process was carried out so that optimal variable selection and estimation methods were obtained: maximum 21.9% and 13.3% improvement by variable selection and maximum 25.8% and 15.2% improvement by estimation methods.