首页|Exact finite-sample bias and MSE reduction in a simple linear regression model with measurement error

Exact finite-sample bias and MSE reduction in a simple linear regression model with measurement error

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  • NETL
  • NSTL
  • Springer Nature
This paper deals with the problem of estimating a slope parameter in a simple linear regression model, where independent variables have functional measurement errors. Measurement errors in independent variables, as is well known, cause biasedness of the ordinary least squares estimator. A general procedure for the bias reduction is presented in a finite sample situation, and some exact bias-reduced estimators are proposed. Also, it is shown that certain truncation procedures improve the mean square errors of the ordinary least squares and the bias-reduced estimators.

Bias correctionErrors-in-variables modelFunctional relationshipMean square errorMultivariate calibration problemRepeated measurementShrinkage estimatorStatistical control problemStatistical decision theoryStructural relationship

Hisayuki Tsukuma

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Faculty of Medicine, Toho University, 5-21-16 Omori-nishi, Ota-ku, Tokyo 143-8540, Japan

2019

10.1007/s42081-018-0025-3

Japanese Journal of Statistics and Data Science

Japanese Journal of Statistics and Data Science

ESCI
ISSN：2520-8756
年,卷(期)：2019.2(1)
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