A new stochastic mixed ridge estimator in linear regression model

Li, Yalian; Yang, Hu

doi:10.1007/s00362-008-0169-5

Cited by 50 publications

(33 citation statements)

References 13 publications

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“…To illustrate the performance of the new estimator, we now consider the data which was discussed in Gruber (1998), and the data has then been widely analyzed in literature by Akdeniz and Erol (2003) and Li and Yang (2010). Table 1 gives Total National Research and Development Expenditures-as a percent of gross national product by country: 1972-1986.…”

Section: Numerical Examplementioning

confidence: 98%

Combining two-parameter and principal component regression estimators

Chang

Yang

2011

Stat Papers

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This paper is concerned with the parameter estimation in linear regression model. To overcome the multicollinearity problem, a new class of estimator, namely principal component two-parameter (PCTP) estimator is proposed. The superiority of the new estimator over the principal component regression (PCR) estimator, the r − k class estimator, the r − d class estimator and the two-parameter estimator proposed by Yang and Chang (Commun Stat Theory Methods 39:923-934 2010) are discussed with respect to the mean squared error matrix (MSEM) criterion. Furthermore, we give a numerical example and a simulation study to illustrate some of the theoretical results.

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Section: Numerical Examplementioning

confidence: 98%

Combining two-parameter and principal component regression estimators

Chang

Yang

2011

Stat Papers

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“…Kaciranlar et al (1998) compared the estimator introduced by Sarkar (1992) and the modified RE based on prior information proposed by Swindel (1976). Also, Ozkale (2009) introduce the stochastic restricted ridge regression estimator in linear models and Li and Yang (2010) derived the MRE.…”

Section: Introductionmentioning

confidence: 99%

The weighted ridge estimator in stochastic restricted linear measurement error models

2016

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In this paper, we introduce the mixed ridge estimator (MRE) in linear measurement error models with stochastic linear restrictions and present the method of weighted mixed ridge estimation, which permits to assign possibly unequal weights to the prior information in relation to the sample information. The performance of the weighted mixed ridge estimator (WMRE) against the weighted mixed estimator (WME) is examined in terms of the mean squared error matrix (MSEM) of estimators. Finally, a simulation study and a numerical example are also given to show the theoretical results.

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“…Based on (14), the respective bias vector, dispersion matrix, and MSEM of the MRE, SRRE, SRAURE, SRLE, SRAULE, SRPCRE, SRrk, and SRrd can easily be obtained and are given in Table B1 in Appendix B.…”

Section: Model Specification and The Estimatorsmentioning

confidence: 99%

“…In this paper, the performance of the recently introduced stochastic restricted estimators, namely, the Stochastic Restricted Ridge Estimator (SRRE) proposed by Li and Yang [14] [17], was examined in the misspecified regression model when multicollinearity exists among explanatory variables. Further, a generalized form to represent these estimators is also proposed.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Restricted Biased Estimators in Misspecified Regression Model with Incomplete Prior Information

Kayanan

Wijekoon

2018

Journal of Probability and Statistics

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The analysis of misspecification was extended to the recently introduced stochastic restricted biased estimators when multicollinearity exists among the explanatory variables. The Stochastic Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted Principal Component Regression Estimator (SRPCRE), Stochastic Restricted r-k (SRrk) class estimator, and Stochastic Restricted r-d (SRrd) class estimator were examined in the misspecified regression model due to missing relevant explanatory variables when incomplete prior information of the regression coefficients is available. Further, the superiority conditions between estimators and their respective predictors were obtained in the mean square error matrix (MSEM) sense. Finally, a numerical example and a Monte Carlo simulation study were used to illustrate the theoretical findings.

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A new stochastic mixed ridge estimator in linear regression model

Abstract: Ordinary ridge estimator, Ordinary mixed estimator, Stochastic mixed ridge estimator, Mean squared error matrix, 62J05, 62F30,

Cited by 50 publications

References 13 publications

Combining two-parameter and principal component regression estimators

Combining two-parameter and principal component regression estimators

The weighted ridge estimator in stochastic restricted linear measurement error models

Stochastic Restricted Biased Estimators in Misspecified Regression Model with Incomplete Prior Information

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