Estimation of slope for measurement error model with equation error: applications on serum kanamycin data

Saqr, Anwar and Khan, Shahjahan (2017) Estimation of slope for measurement error model with equation error: applications on serum kanamycin data. In: 3rd ISM International Statistical Conference 2016 (ISM III), 9-11 Aug 2016, Kuala Lumpur, Malaysia .

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Abstract

This paper introduces a statistical method to estimate the parameters of bivariate structural errors-
in-variables model (EIV). It is a complex problem when there is no or uncertain prior knowledge of the
measurement errors variances. The proposed estimators of the parameters of EIV model are derived
based on mathematical modi�cation method for observed data. This method is suggested to reproduce
an explanatory variable that has equivalent statistical characteristics of the unobserved explanatory vari-
able, and to correct for the effects of measurement error in predictors. The proposed method produce
robust estimators, and it is straightforward, easy to implement, and takes into account the equation
errors. The simulation studies show that the new estimator to be generally more efficient and less biased
than some other previous approaches. Compared to the maximum likelihood method via the simulation
studies, the estimators of the proposed method are nearly asymptotically unbiased and efficient when
there is no or uncertain prior knowledge of the measurement errors variances. The numerical comparisons
of the simulation studies results are included.In addition, results are illustrated with applications on one
well-known real data sets of serum kanamycin.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 31 Jan 2017 08:15
Last Modified: 16 May 2017 00:06
Uncontrolled Keywords: linear regression models, errors-in-variables, reliability ratio, mathematical modifification
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
01 Mathematical Sciences > 0104 Statistics > 010499 Statistics not elsewhere classified
01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
URI: http://eprints.usq.edu.au/id/eprint/30462

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