MSE Performance and Minimax Regret Significance Points for a HPT Estimator when each Individual Regression Coefficient is Estimated

Xu, Haifeng

doi:10.1080/03610926.2011.605239

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The exact formulae of H 1 (2r, r; c|σ), H 2 (0, r; c|σ), J 1 (2r + 1, r; c|σ) and J 2 (0, r; c|σ) in Equations (A1) and (A2) have been shown in Xu (2012b). Using Equations (2), (3) and (4), the exact formula for the case of the even moments can be written as follow.…”

Section: Accepted Manuscriptmentioning

confidence: 97%

“…In this article, assuming that the error terms follow a multivariate t distribution, we derived the exact formulae for the moments of the HPT estimator proposed by Xu (2012b). We also executed the numerical evaluation to investigate the MSE performance of the HPT estimator, and compared it with those of the FRR estimator and the usual OLS estimator.…”

Section: Summary and Concluding Remarksmentioning

confidence: 98%

“…In this paper, assuming that the error terms follow a multivariate t distribution, we consider the HPT estimator proposed by Xu (2012b), derive the exact formulae for the moments of the HPT estimator, and investigate the MSE performance of the HPT estimator using numerical evaluations.…”

Section: Introductionmentioning

confidence: 99%

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MSE performance and minimax regret significance points for a HPT estimator under a multivariate t error distribution

2016

Communications in Statistics - Theory and Methods

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In this paper, assuming that the error terms follow a multivariate t distribution, we derive the exact formulae for the moments of the heterogeneous preliminary test (HPT) estimator proposed by Xu (2012b). We also execute the numerical evaluation to investigate the MSE performance of the HPT estimator, and compare it with those of the feasible ridge regression (FRR) estimator and the usual ordinary least squared (OLS) estimator. Further, we derive the optimal critical values of the preliminary F test for the HPT estimator, using the minimax regret function proposed by Sawa and Hiromatsu (1973). Our results show that: 1. the optimal significance level (α * ) increases as the degrees of freedom of multivariate t distribution (ν 0 ) increases; 2. when ν 0 ≥ 10, the value of α * is close to that in the normal error case.

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Section: Accepted Manuscriptmentioning

confidence: 97%

Section: Summary and Concluding Remarksmentioning

confidence: 98%