2021
DOI: 10.1080/00949655.2021.1885671
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Defining a two-parameter estimator: a mathematical programming evidence

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Cited by 6 publications
(7 citation statements)
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“…Toker et al 36 developed the following censored log‐likelihood function by adding a penalization term to Equation () QTLEgoodbreak=logL()X;β,σ2goodbreak+12σ2false(βdβtrue^false)(βgoodbreak−dtrueβ^),$$ {Q}_{TLE}=\log L\left(X;\beta, {\sigma}^2\right)+\frac{1}{2{\sigma}^2}{\left(\beta -d\hat{\beta}\right)}^{\prime}\left(\beta -d\hat{\beta}\right), $$ where 12σ2$$ \frac{1}{2{\sigma}^2} $$ is a Lagrangian multiplier, d$$ d $$ is the Liu biasing parameter in the interval (0,1)$$ \left(0,1\right) $$ and trueβ^$$ \hat{\beta} $$ is the T‐MLE attained in the final iteration. Although Toker et al 36 took the interval of d$$ d $$ as 0<d<1$$ 0<d<1 $$, this interval can be extended to normal∞<d<normal∞$$ -\infty <d<\infty $$ (see 26,57 ). With the help of Equation (), they obtained the final form of the T‐LE as βtrue^TLEfalse(hfalse)=βtrue^TLEfalse(h1false)…”
Section: Tobit Regression Model and Some Estimatorsmentioning
confidence: 99%
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“…Toker et al 36 developed the following censored log‐likelihood function by adding a penalization term to Equation () QTLEgoodbreak=logL()X;β,σ2goodbreak+12σ2false(βdβtrue^false)(βgoodbreak−dtrueβ^),$$ {Q}_{TLE}=\log L\left(X;\beta, {\sigma}^2\right)+\frac{1}{2{\sigma}^2}{\left(\beta -d\hat{\beta}\right)}^{\prime}\left(\beta -d\hat{\beta}\right), $$ where 12σ2$$ \frac{1}{2{\sigma}^2} $$ is a Lagrangian multiplier, d$$ d $$ is the Liu biasing parameter in the interval (0,1)$$ \left(0,1\right) $$ and trueβ^$$ \hat{\beta} $$ is the T‐MLE attained in the final iteration. Although Toker et al 36 took the interval of d$$ d $$ as 0<d<1$$ 0<d<1 $$, this interval can be extended to normal∞<d<normal∞$$ -\infty <d<\infty $$ (see 26,57 ). With the help of Equation (), they obtained the final form of the T‐LE as βtrue^TLEfalse(hfalse)=βtrue^TLEfalse(h1false)…”
Section: Tobit Regression Model and Some Estimatorsmentioning
confidence: 99%
“…Aslam and Ahmad 56 proposed a new two‐parameter estimator by modifying the LE using the existing modified ridge‐type estimator. Most recently, Üstündağ Şiray et al 57 developed a new two‐parameter estimator which has many benefits from different aspects in the linear regression model since it combines the advantages of the RE and LE. Also, they investigated the selection of the shrinkage parameters utilizing both conventional and contemporary methods.…”
Section: Introductionmentioning
confidence: 99%
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“… The use of “hat” is missing while presenting some estimators. In the Introduction section, the manuscript Defining a two-parameter estimator: a mathematical programming evidence by Üstündağ Şiray et al (2021) 1 may be mentioned since this is a more recent article in which a new biased estimator is proposed to mitigate multicollinearity. On page 4, the authors should explain what lambdas are.…”
mentioning
confidence: 99%