Efficiency of a stochastic restricted two-parameter estimator in linear regression

Li, Yalian; Yang, Hu

doi:10.1016/j.amc.2014.10.011

Cited by 9 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By providing a reduction in bias, Wu and Yang 45 recommended an almost unbiased two‐parameter estimator. By combining the ideas behind mixed estimator and the k‐d class estimator, Li and Yang 46 introduced a stochastic restricted two‐parameter estimator when stochastic linear restrictions are assumed to hold. Principal component Liu‐type estimator was introduced by Wu and Yang 47 .…”

Section: Introductionmentioning

confidence: 99%

Two‐parameter estimation forTobitmodel: An application to national health and nutrition examination survey dataset

Özbay

Şiray

Toker

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Fixed censorship is usually encountered in medical fields, as well as biological, econometric, and engineering researches. It is quite possible to come across censored data with multicollinearity in real-life studies. The multicollinearity affects traditional Tobit maximum likelihood estimation, which is often used in presence of censored data.Biased estimators such as Tobit Liu estimator with one biasing parameter can be used for solving the multicollinearity. In this study, Tobit two-parameter estimator with two biasing parameters is proposed as an alternative to the classical Tobit maximum likelihood and Tobit Liu estimators for the censored data. This new estimator provides advantages from two different aspects thanks to having two biasing parameters. With a simulation study consisting of several scenarios where various levels of factors like the number of independent variables, sample size, correlation degree, variance, and censorship are examined, an in-depth experimental analysis is conducted. In these scenarios, two different biasing parameters' selection methods where one of the biasing parameters is fixed to determine the other one are used and their corresponding outputs are obtained. In addition, a real-life application is carried out with the National Health and Nutrition Examination Survey public-use dataset which includes some demographic and health-related laboratory measurements. The simulation study and data analysis results show that the proposed estimator is more advantageous to its competitors.

show abstract

Section: Introductionmentioning

confidence: 99%

Two‐parameter estimation forTobitmodel: An application to national health and nutrition examination survey dataset

Özbay

Şiray

Toker

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Some statisticians are discussed how to deal with collinearity. One method is to consider the biased estimator, such as Stein [4]; Hoerl and Kennard [5]; Liu [6]; Yang and Chang [7]; and Wu and Yang [8] et al Another method is to consider the linear restriction and stochastic linear restrictions [9], such as Li and Yang et al [10].…”

Section: Introductionmentioning

confidence: 99%

The Mixed Liu Estimator in Stochastic Restricted Linear Measurement Error Model

2021

Journal of Mathematics

View full text Add to dashboard Cite

Ghapani and Babdi [1] proposed a mixed Liu estimator in linear measurement error model with stochastic linear restrictions. In this article, we propose an alternative mixed Liu estimator in the linear measurement error model with stochastic linear restrictions. The performance of the new mixed Liu estimator over the mixed estimator, Liu estimator, and mixed Liu estimator proposed by Ghapani and Babdi [1] are discussed in the sense of mean squared error matrix. Finally, a simulation study is given to show the performance of these estimators.

show abstract