• Title/Summary/Keyword: Ridge Estimators

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Bootstrap Confidence Intervals of Ridge Estimators in Mixture Experiments (혼합물실험에서 능형추정량에 대한 붓스트랩 신뢰구간)

  • Jang, Dae-Heung
    • Journal of Korean Society for Quality Management
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    • v.34 no.3
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    • pp.62-65
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    • 2006
  • We can use the ridge regression as a means for stabilizing the coefficient estimators in the fitted model when performing experiments in highly constrained regions causes collinearity problems in mixture experiments. But there is no theory available on which to base statistical inference of ridge estimators. The bootstrap could be used to seek the confidence intervals of ridge estimators.

Two Bootstrap Confidence Intervals of Ridge Regression Estimators in Mixture Experiments (혼합물실험에서 능형회귀추정량에 대한 두 종류의 붓스트랩 신뢰구간)

  • Jang Dae-Heung
    • The Korean Journal of Applied Statistics
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    • v.19 no.2
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    • pp.339-347
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    • 2006
  • In mixture experiments, performing experiments in highly constrained regions causes collinearity problems. We can use the ridge regression as a means for stabilizing the coefficient estimators in the fitted model. But there is no theory available on which to base statistical inference of ridge estimators. The bootstrap technique could be used to seek the confidence intervals for ridge estimators.

The Use Ridge Regression for Yield Prediction Models with Multicollinearity Problems (수확예측(收穫豫測) Model의 Multicollinearity 문제점(問題點) 해결(解決)을 위(爲)한 Ridge Regression의 이용(利用))

  • Shin, Man Yong
    • Journal of Korean Society of Forest Science
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    • v.79 no.3
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    • pp.260-268
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    • 1990
  • Two types of ridge regression estimators were compared with the ordinary least squares (OLS) estimator in order to select the "best" estimator when multicollinearitc existed. The ridge estimators were Mallows's (1973) $C_P$-like statistic, and Allen's (1974) PRESS-like statistic. The evaluation was conducted based on the predictive ability of a yield model developed by Matney et al. (1988). A total of 522 plots from the data of the Southwide Loblolly Pine Seed Source study was used in this study. All of ridge estimators were better in predictive ability than the OLS estimator. The ridge estimator obtained by using Mallows's statistic performed the best. Thus, ridge estimators can be recommended as an alternative estimator when multicollinearity exists among independent variables.

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The $m^{th}$ Moment of Generalized Ridge Estimators

  • Kim, Ju-Sung
    • Journal of the Korean Statistical Society
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    • v.12 no.1
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    • pp.18-23
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    • 1983
  • Dwivedi, Srivastava and Hall(1980) derived the first and second moments of generalized ridge estimators. In this paper we consider the $m^{th}$ moment of a generalized ridge estimator and tabulate tis skewness measure.

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ROBUST CROSS VALIDATIONS IN RIDGE REGRESSION

  • Jung, Kang-Mo
    • Journal of applied mathematics & informatics
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    • v.27 no.3_4
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    • pp.903-908
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    • 2009
  • The shrink parameter in ridge regression may be contaminated by outlying points. We propose robust cross validation scores in ridge regression instead of classical cross validation. We use robust location estimators such as median, least trimmed squares, absolute mean for robust cross validation scores. The robust scores have global robustness. Simulations are performed to show the effectiveness of the proposed estimators.

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Comparison of Lasso Type Estimators for High-Dimensional Data

  • Kim, Jaehee
    • Communications for Statistical Applications and Methods
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    • v.21 no.4
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    • pp.349-361
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    • 2014
  • This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

CONFLICT AMONG THE SHRINKAGE ESTIMATORS INDUCED BY W, LR AND LM TESTS UNDER A STUDENT'S t REGRESSION MODEL

  • Kibria, B.M.-Golam
    • Journal of the Korean Statistical Society
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    • v.33 no.4
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    • pp.411-433
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    • 2004
  • The shrinkage preliminary test ridge regression estimators (SPTRRE) based on Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests for estimating the regression parameters of the multiple linear regression model with multivariate Student's t error distribution are considered in this paper. The quadratic biases and risks of the proposed estimators are compared under both null and alternative hypotheses. It is observed that there is conflict among the three estimators with respect to their risks because of certain inequalities that exist among the test statistics. In the neighborhood of the restriction, the SPTRRE based on LM test has the smallest risk followed by the estimators based on LR and W tests. However, the SPTRRE based on W test performs the best followed by the LR and LM based estimators when the parameters move away from the subspace of the restrictions. Some tables for the maximum and minimum guaranteed efficiency of the proposed estimators have been given, which allow us to determine the optimum level of significance corresponding to the optimum estimator among proposed estimators. It is evident that in the choice of the smallest significance level to yield the best estimator the SPTRRE based on Wald test dominates the other two estimators.

A Ridge-type Estimator For Generalized Linear Models (일반화 선형모형에서의 능형형태의 추정량)

  • Byoung Jin Ahn
    • The Korean Journal of Applied Statistics
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    • v.7 no.1
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    • pp.75-82
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    • 1994
  • It is known that collinearity among the explanatory variables in generalized linear models inflates the variance of maximum likelihood estimators. A ridge-type estimator is presented using penalized likelihood. A method for choosing a shrinkage parameter is discussed and this method is based on a prediction-oriented criterion, which is Mallow's $C_L$ statistic in a linear regression setting.

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A study on the properties of sensitivity analysis in principal component regression and latent root regression (주성분회귀와 고유값회귀에 대한 감도분석의 성질에 대한 연구)

  • Shin, Jae-Kyoung;Chang, Duk-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.2
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    • pp.321-328
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    • 2009
  • In regression analysis, the ordinary least squares estimates of regression coefficients become poor, when the correlations among predictor variables are high. This phenomenon, which is called multicollinearity, causes serious problems in actual data analysis. To overcome this multicollinearity, many methods have been proposed. Ridge regression, shrinkage estimators and methods based on principal component analysis (PCA) such as principal component regression (PCR) and latent root regression (LRR). In the last decade, many statisticians discussed sensitivity analysis (SA) in ordinary multiple regression and same topic in PCR, LRR and logistic principal component regression (LPCR). In those methods PCA plays important role. Many statisticians discussed SA in PCA and related multivariate methods. We introduce the method of PCR and LRR. We also introduce the methods of SA in PCR and LRR, and discuss the properties of SA in PCR and LRR.

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