Comparison of Two Estimators of Parameters Under Pitman Nearness Criterion

Yang, Hu; Li, Wenxue; Xu, Jianwen

doi:10.1080/03610920903181985

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Since a meeting held by Rao and kentry In literature, many authors have compared estimators using PC criterion. Wang and Yang (1994) used the PC criterion to compare two linear estimators in linear regression model and Reif (2006) used PC criterion to compare general pre-test estimators with some regression estimators. Yang et al (2010) used it to compare two unified biased estimators in the linear regression model.…”

Section: Define Andmentioning

confidence: 99%

Estimation of the parameters in two linear models with some of the identical parameter vectors under the Pitman's closeness criterion

2019

J. Natn. Sci. Foundation Sri Lanka

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Two normal linear models for some of the identical parameters are discussed in this article. Many authors have studied the properties of estimators in two normal linear models for some of the identical parameters using mean squared error and mean squared error matrix criteria. In this article we give comparison between the estimators in two normal linear models with some identical parameters under Pitman's closeness criterion with known variances. It is noted that the estimators in two linear models with some identical parameters with known variances are superior over the estimators in single linear models with some identical parameters with known variances. Finally, the simulation is carried out to show that the theoretical results which we obtained in this study are aligned with different simulation conditions. The simulation results agree with our theoretical results.

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Section: Define Andmentioning

confidence: 99%

Estimation of the parameters in two linear models with some of the identical parameter vectors under the Pitman's closeness criterion

2019

J. Natn. Sci. Foundation Sri Lanka

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“…Reif [12] compared general pre-test estimator with some regression estimator under PC criterion. Yang et al [13] compared united biased estimators in linear model. Özkale and Kaçıranlar (2008) [10,14] and Li et al [15] compared r − k class estimator with ordinary least squares estimator under PC criterion.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Estimators of the Regression Coefficient Vector Under Pitman’s Closeness Criterion

Wu¹

2020

Fuzzy Systems and Data Mining VI

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Schaffrin and Toutenburg [1] proposed a weighted mixed estimation based on the sample information and the stochastic prior information, and they also show that the weighted mixed estimator is superior to the ordinary least squares estimator under the mean squared error criterion. However, there has no paper to discuss the performance of the two estimators under the Pitman’s closeness criterion. This paper presents the comparison of the weighted mixed estimator and the ordinary least squares estimator using the Pitman’s closeness criterion. A simulation study is performed to illustrate the performance of the weighted mixed estimator and the ordinary least squares estimator under the Pitman’s closeness criterion.

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“…Rao [8] has discussed the similarities and differences of mean squared error and PC and has aroused great interest in PC. The monograph by Keating et al [9] provided an illuminating account of PC and a long list of publications on comparisons of estimators of scalar functions of univariate parameters [10]. After that, many authors have used PC to compare estimators, such as, Wencheko [11] who compared some estimators under the PC criterion in linear regression model, Yang et al [10] compared two linear estimators under the PC criterion, and Ahmadi and Balakrishnan [12,13] compared some order statistic under the PC criterion.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model

2014

Journal of Applied Mathematics

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Batah et al. (2009) combined the unbiased ridge estimator and principal components regression estimator and introduced the modified -class estimator. They also showed that the modified -class estimator is superior to the ordinary least squares estimator and principal components regression estimator in the mean squared error matrix. In this paper, firstly, we will give a new method to obtain the modified -class estimator; secondly, we will discuss its properties in some detail, comparing the modified -class estimator to the ordinary least squares estimator and principal components regression estimator under the Pitman closeness criterion. A numerical example and a simulation study are given to illustrate our findings.

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Comparison of Two Estimators of Parameters Under Pitman Nearness Criterion

Cited by 7 publications

References 10 publications

Estimation of the parameters in two linear models with some of the identical parameter vectors under the Pitman's closeness criterion

Estimation of the parameters in two linear models with some of the identical parameter vectors under the Pitman's closeness criterion

Comparison of Two Estimators of the Regression Coefficient Vector Under Pitman’s Closeness Criterion

Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model

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