A New Two-Parameter Estimator in Linear Regression

Yang, Hu; Chang, Xinfeng

doi:10.1080/03610920902807911

Cited by 102 publications

(64 citation statements)

References 16 publications

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“…Sakallioglu and Kaciranlar (2008) proposed a new biased on RR estimator for the vector of parameters in a linear regression model. By combining the RR estimator and the Liu estimator, Yang and Chang (2010) proposed the twoparameter (TP) estimator, which include the OLS, RR and Liu estimators as special cases.…”

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confidence: 99%

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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confidence: 99%

Combining two-parameter and principal component regression estimators

Chang

Yang

2011

Stat Papers

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“…Kaçiranlar and Sakallıoglu [8] proposed − class estimator which is a combination of the LE and PCRE and showed the superiority of the − class estimator over the OLSE, LE, and PCRE.Özkale and Kaçıranlar [9] proposed two-parameter estimator (TPE) by utilizing the advantages of the ORRE and LE and obtained necessary and sufficient condition for dominance of the TPE over the OLSE in MSE matrix sense. Further, Yang and Chang [10] also combined the ORR and Liu estimator in a different way and introduced an another two-parameter estimator (ATPE) and 2 International Journal of Mathematics and Mathematical Sciences derived necessary and sufficient conditions for superiority of the ATPE over OLSE, ORRE, LE, and TPE under MSE matrix criterion.Özkale [11] put forward a general class of estimators, − ( , ) class estimator which is a mingle of the TPE [9] and PCRE; they evaluated the performance of the − ( , ) class estimator under MSE criterion. Chang and Yang [12] suggested another general class of estimators by merging the PCRE and ATPE [10] named as principal component twoparameter estimator (PCTPE) and analyzed its performance under MSE matrix sense.…”

Section: Introductionmentioning

confidence: 99%

“…Further, Yang and Chang [10] also combined the ORR and Liu estimator in a different way and introduced an another two-parameter estimator (ATPE) and 2 International Journal of Mathematics and Mathematical Sciences derived necessary and sufficient conditions for superiority of the ATPE over OLSE, ORRE, LE, and TPE under MSE matrix criterion.Özkale [11] put forward a general class of estimators, − ( , ) class estimator which is a mingle of the TPE [9] and PCRE; they evaluated the performance of the − ( , ) class estimator under MSE criterion. Chang and Yang [12] suggested another general class of estimators by merging the PCRE and ATPE [10] named as principal component twoparameter estimator (PCTPE) and analyzed its performance under MSE matrix sense.…”

Section: Introductionmentioning

confidence: 99%

A Note on the Performance of Biased Estimators with Autocorrelated Errors

Tyagi

Chandra

2017

International Journal of Mathematics and Mathematical Sciences

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It is a well-established fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on the properties of the least squares estimator. Huang and Yang (2015) and Chandra and Tyagi (2016) studied the PCTP estimator and the − ( , ) class estimator, respectively, to deal with both problems simultaneously and compared their performances with the estimators obtained as their special cases. However, to the best of our knowledge, the performance of both estimators has not been compared so far. Hence, this paper is intended to compare the performance of these two estimators under mean squared error (MSE) matrix criterion. Further, a simulation study is conducted to evaluate superiority of the − ( , ) class estimator over the PCTP estimator by means of percentage relative efficiency. Furthermore, two numerical examples have been given to illustrate the performance of the estimators.

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“…introduced the two-parameter (TP) estimator for the vector of parameters in a linear regression model [7]. Yang and Chang (2010) proposed a new two-parameter estimator which includes the ordinary least squares (OLS) estimator, the ridge regression (RR) estimator and the Liu estimator as special case [8].…”

Section: Introductionmentioning

confidence: 99%

Fitting Characteristics of the Two-Parameter Estimator in Linear Regression

Chang¹

2016

Proceedings of the 3d International Conference on Applied Social Science Research

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- introduced the twoparameter (TP) estimator for the vector of parameters in a linear regression model. In this paper, several better fitting characteristics of the TP estimator than those of the ordinary least squares and ridge estimators are considered. Furthermore, we propose and investigate the prediction error sum of squares (PRESS) statistics for selecting the parameter d of the TP estimator.

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A New Two-Parameter Estimator in Linear Regression

Cited by 102 publications

References 16 publications

Combining two-parameter and principal component regression estimators

Combining two-parameter and principal component regression estimators

A Note on the Performance of Biased Estimators with Autocorrelated Errors

Fitting Characteristics of the Two-Parameter Estimator in Linear Regression

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