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Year 2017, Volume: 66 Issue: 2, 311 - 322, 01.08.2017
https://doi.org/10.1501/Commua1_0000000821

Abstract

References

  • Carroll, R.J & Stefanski, L.A. , Approximate quasilikelihood estimation in models with sur- rogate predictors, Journal of the American Statistical Association, (1990), 85, pp. 652-663.
  • Carroll, R.J., Ruppert, D., Stefanski, L.A.& Crainiceanu, C.M., Measurement Error in Non- linear Models, 2nd edn.Chapman & Hall/CRC 2006.
  • Casella, G. & Berger, R. L. Statistical Inference, Duxbury Press, Belmont, 1990.
  • Cook, J.R. & Stefanski, L.A., Simulation Extrapolation Estimation in Parametric Measure- ment Error Models, Journal of the American Statistical Association, (1994), 89, pp. 1314-1328.
  • Fuller, W.A., Measurement Error Models, John Wiley and Sons, New York, 1987.
  • Gleser, L.J., Improvements of the naive approach to estimation in nonlinear errors-in-variables regression models. In Statistical Analysis of Error Measurement Models and Application, P. J. Brown and W. A. Fuller, ed., Providence: American Mathematics Society, 1990.
  • Ser*ing, R.J., Approximation Theorems of Mathematical Statistics, John Wiley and Sons, Singapore, 1980.
  • Stefanski, L.A. & Cook, J.R., Simulation-Extrapolation: The Measurement Error, Journal of the American Statistical Association, (1995), 90, pp. 1247-1256. Current address : Rukiye E. Da¼galp (Corresponding author): Ankara University, Faculty of
  • Sciences, Department of Statistics, 06100 Tando¼gan-Ankara/Turkey. E-mail address : rdagalp@ankara.edu.tr Current address : ·Ihsan Karabulut:Ankara University, Faculty of Sciences, Department of Sta
  • tistics, 06100 Tando¼gan-Ankara/Turkey. E-mail address : kbulut@science.ankara.edu.tr Current address : Fikri Öztürk:Ankara University, Faculty of Sciences, Department of Statis
  • tics, 06100 Tando¼gan-Ankara/Turkey. E-mail address : ozturk@science.ankara.edu.tr

Estimation methods for simple linear regression with measurement error: a real data application

Year 2017, Volume: 66 Issue: 2, 311 - 322, 01.08.2017
https://doi.org/10.1501/Commua1_0000000821

Abstract

The classical measurement error model is discussed in the context of parameter estimation of the simple linear regression. The attenuationeğect of measurement error on the parameter estimation is eliminated usingthe regression calibration and simulation extrapolation methods. The massdensity of pebbles population is investigated as a real data application. Themass and volume of a pebble are regarded an error-free and error-prone variables, respectively. The population mass density is considered to be the slopeparameter of the simple linear regression without intercept

References

  • Carroll, R.J & Stefanski, L.A. , Approximate quasilikelihood estimation in models with sur- rogate predictors, Journal of the American Statistical Association, (1990), 85, pp. 652-663.
  • Carroll, R.J., Ruppert, D., Stefanski, L.A.& Crainiceanu, C.M., Measurement Error in Non- linear Models, 2nd edn.Chapman & Hall/CRC 2006.
  • Casella, G. & Berger, R. L. Statistical Inference, Duxbury Press, Belmont, 1990.
  • Cook, J.R. & Stefanski, L.A., Simulation Extrapolation Estimation in Parametric Measure- ment Error Models, Journal of the American Statistical Association, (1994), 89, pp. 1314-1328.
  • Fuller, W.A., Measurement Error Models, John Wiley and Sons, New York, 1987.
  • Gleser, L.J., Improvements of the naive approach to estimation in nonlinear errors-in-variables regression models. In Statistical Analysis of Error Measurement Models and Application, P. J. Brown and W. A. Fuller, ed., Providence: American Mathematics Society, 1990.
  • Ser*ing, R.J., Approximation Theorems of Mathematical Statistics, John Wiley and Sons, Singapore, 1980.
  • Stefanski, L.A. & Cook, J.R., Simulation-Extrapolation: The Measurement Error, Journal of the American Statistical Association, (1995), 90, pp. 1247-1256. Current address : Rukiye E. Da¼galp (Corresponding author): Ankara University, Faculty of
  • Sciences, Department of Statistics, 06100 Tando¼gan-Ankara/Turkey. E-mail address : rdagalp@ankara.edu.tr Current address : ·Ihsan Karabulut:Ankara University, Faculty of Sciences, Department of Sta
  • tistics, 06100 Tando¼gan-Ankara/Turkey. E-mail address : kbulut@science.ankara.edu.tr Current address : Fikri Öztürk:Ankara University, Faculty of Sciences, Department of Statis
  • tics, 06100 Tando¼gan-Ankara/Turkey. E-mail address : ozturk@science.ankara.edu.tr
There are 11 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

E.rukiye Dağalp This is me

İhsan Karabulut This is me

Fikri Öztürk This is me

Publication Date August 1, 2017
Published in Issue Year 2017 Volume: 66 Issue: 2

Cite

APA Dağalp, E., Karabulut, İ., & Öztürk, F. (2017). Estimation methods for simple linear regression with measurement error: a real data application. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 66(2), 311-322. https://doi.org/10.1501/Commua1_0000000821
AMA Dağalp E, Karabulut İ, Öztürk F. Estimation methods for simple linear regression with measurement error: a real data application. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. August 2017;66(2):311-322. doi:10.1501/Commua1_0000000821
Chicago Dağalp, E.rukiye, İhsan Karabulut, and Fikri Öztürk. “Estimation Methods for Simple Linear Regression With Measurement Error: A Real Data Application”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 66, no. 2 (August 2017): 311-22. https://doi.org/10.1501/Commua1_0000000821.
EndNote Dağalp E, Karabulut İ, Öztürk F (August 1, 2017) Estimation methods for simple linear regression with measurement error: a real data application. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 66 2 311–322.
IEEE E. Dağalp, İ. Karabulut, and F. Öztürk, “Estimation methods for simple linear regression with measurement error: a real data application”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 66, no. 2, pp. 311–322, 2017, doi: 10.1501/Commua1_0000000821.
ISNAD Dağalp, E.rukiye et al. “Estimation Methods for Simple Linear Regression With Measurement Error: A Real Data Application”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 66/2 (August 2017), 311-322. https://doi.org/10.1501/Commua1_0000000821.
JAMA Dağalp E, Karabulut İ, Öztürk F. Estimation methods for simple linear regression with measurement error: a real data application. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2017;66:311–322.
MLA Dağalp, E.rukiye et al. “Estimation Methods for Simple Linear Regression With Measurement Error: A Real Data Application”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 66, no. 2, 2017, pp. 311-22, doi:10.1501/Commua1_0000000821.
Vancouver Dağalp E, Karabulut İ, Öztürk F. Estimation methods for simple linear regression with measurement error: a real data application. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2017;66(2):311-22.

Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.

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