Performance of the difference-based almost unbiased Liu estimator in partial linear model

Wu, Jibo

doi:10.1080/00949655.2015.1136628

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…When order of differences coefficients increased we find that the partially linear models with proposed estimators at the first mattress especially at the differences coefficients of the fifth -order and when the variance is small. In general, we can observe the order of the methods according to sample sizes as shown in Table (10).…”

Section: Discussionmentioning

confidence: 99%

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Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Hussein¹

2019

Baghdad Sci.J

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In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

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Section: Discussionmentioning

confidence: 99%

“…Now, in this paper when ridge parameter (k) is variable for the diameter elements of the information matrix ) ' ( X X , by applying differences technique In the same way that others (14,20,15,24,10) have applied the technique of differences to the model ( 1…”

Section: Ols Mjrmentioning

confidence: 99%

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Hussein¹

2019

Baghdad Sci.J

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On the Performance of the r-k Class Estimator in Partial Linear Model

2019

Proceedings of the 2019 4th International Conference on Big Data and Computing - ICBDC 2019

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A generalized Liu-type estimator for logistic partial linear regression model with multicollinearity

Dai¹,

Wang

2023

MATH

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<abstract><p>This paper is concerned with proposing a generalized Liu-type estimator (GLTE) to address the multicollinearity problem of explanatory variable of the linear part in the logistic partially linear regression model. Using the profile likelihood method, we propose the GLTE as a general class of Liu-type estimator, which includes the profile likelihood estimator, the ridge estimator, the Liu estimator and the Liu-type estimator as special cases. The conditional superiority of the proposed GLTE over the other estimators is derived under the asymptotic mean square error matrix (MSEM) criterion. Moreover, the optimal choices of biasing parameters and function of biasing parameter are given. Numerical simulations demonstrate that the proposed GLTE performs better than the existing estimators. An application on a set of real data arising from the study of Indian Liver Patient is shown for illustrating our theoretical results.</p></abstract>

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Performance of the difference-based almost unbiased Liu estimator in partial linear model

Cited by 3 publications

References 13 publications

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

On the Performance of the r-k Class Estimator in Partial Linear Model

A generalized Liu-type estimator for logistic partial linear regression model with multicollinearity

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