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1. www.tandfonline.com › doi › fullOn the jackknife Kibria-Lukman estimator for the linear ...

   www.tandfonline.com › doi › full

   Nov 26, 2021 · In this paper, we developed a Jackknifed version of the Kibria-Lukman estimator- the estimator is named the Jackknifed KL estimator (JKLE). We derived the statistical properties of the new estimator and compared it theoretically with the KLE and some other existing estimators.

2. www.frontiersin.org › journals › applied-mathematicsFrontiers | Generalized Kibria-Lukman Estimator: Method ...

   www.frontiersin.org › journals › applied-mathematics

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   The Kibria-Lukman (KL) estimator is a recent estimator that has been proposed to solve the multicollinearity problem. In this paper, a generalized version of the KL estimator is proposed, along with the optimal biasing parameter of our proposed estimator derived by minimizing the scalar mean squared error.

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4. www.researchgate.net › publication › 360932190(PDF) Jackknife Kibria-Lukman M-Estimator: Simulation and ...

   www.researchgate.net › publication › 360932190

   May 29, 2022 · This study proposed the Robust Jackknife Kibria-Lukman (RJKL) estimator based on the M-estimator to deal with multicollinearity and outliers. We examine the superiority of the estimator...

5. www.researchgate.net › publication › 360095086Generalized Kibria-Lukman Estimator: Method, Simulation, and ...

   www.researchgate.net › publication › 360095086

   Apr 1, 2022 · To eliminate the adverse effects of multicollinearity in the negative binomial regression model, we propose the use of a jackknife version of the Kibria–Lukman estimator.

6. www.researchgate.net › publication › 356566984_OnOn the jackknife Kibria-Lukman estimator for the linear ...

   www.researchgate.net › publication › 356566984_On

   Nov 26, 2021 · The Kibria-Lukman estimator (KLE) was suggested as an alternative to the OLSE and some other estimators (ridge and Liu estimators). In this paper, we developed a Jackknifed version of the...

7. www.philstat.org › special_issue › indexJackknifed K-L estimator in Bell regression model - philstat.org

   www.philstat.org › special_issue › index

   In this paper, a jackknifed version of the K-L estimator in the Bell regression model is proposed, which combines the Jackknife process with the K-L estimator to reduce biasedness.

8. People also ask

   What is a jackknife Kibria-Lukman estimator?

   • On the jackknife Kibria-Lukman estimator .... The linear regression model explores the relationship between the dependent variable and the independent variables. The ordinary least squared estimator (OLSE) is widely applicable to estimate the parameters of the model.

   On the jackknife Kibria-Lukman estimator for the linear regression

   www.tandfonline.com/doi/full/10.1080/03610918.2021.2007401

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   What is the Kibria-Lukman estimator?

   • The Kibria-Lukman (KL) estimator is a recent estimator that has been proposed to solve the multicollinearity problem. In this paper, a generalized version of the KL estimator is proposed, along with the optimal biasing parameter of our proposed estimator derived by minimizing the scalar mean squared error.

   (PDF) Generalized Kibria-Lukman Estimator: Method, Simulation, and

   www.researchgate.net/publication/360095086_Generalized_Kibria-Lukman_Estimator_Method_Simulation_and_Application

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   Can a Kibria-Lukman estimator solve a multicollinearity problem?

   • In the linear regression model, the multicollinearity effects on the ordinary least squares (OLS) estimator performance make it inefficient. To solve this, several estimators are given. The Kibria-Lukman (KL) estimator is a recent estimator that has been proposed to solve the multicollinearity problem.

   (PDF) Generalized Kibria-Lukman Estimator: Method, Simulation, and

   www.researchgate.net/publication/360095086_Generalized_Kibria-Lukman_Estimator_Method_Simulation_and_Application

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   Which jackknife is best for estimating standard error?

   • Roughly speaking, it is preferable to choose a d such that the delete-d jackknife estimation of standard error. Bootstrap is the most recently developed method to estimate errors and other statistics. It requires the much greater power that modern computers can provide. Example. Consider a sample x ( x , x ,..., distribution (or from a population).

   Jackknife - University of California, Berkeley

   www.stat.berkeley.edu/~hhuang/STAT152/Jackknife-Bootstrap.pdf

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   What is a jackknife sample?

   • One of the earliest techniques to obtain reliable statistical estimators is the jackknife technique. It requires less computational power than more recent techniques. ( x ) . The jackknife , called jackknife samples. The ith jackknife sample consists of the data set with the ith observation removed. Let ith jackknife replication of .

   Jackknife - University of California, Berkeley

   www.stat.berkeley.edu/~hhuang/STAT152/Jackknife-Bootstrap.pdf

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   Does jackknife work?

   • The jackknife only works well for linear statistics (e.g., mean). It fails to give accurate estimation for non-smooth (e.g., median) and nonlinear (e.g., correlation coefficient) cases. Thus improvements to this technique were developed. Instead of leaving out one observation at a time, we leave out d observations.

   Jackknife - University of California, Berkeley

   www.stat.berkeley.edu/~hhuang/STAT152/Jackknife-Bootstrap.pdf

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9. www.stat.berkeley.edu › ~hhuang › STAT152Jackknife - University of California, Berkeley

   www.stat.berkeley.edu › ~hhuang › STAT152

   Jackknife. One of the earliest techniques to obtain reliable statistical estimators is the jackknife technique. It requires less computational power than more recent techniques. Suppose we have a sample x ( x , x ,..., 2. x ) and an estimator.