Skip to main content
SpringerLink
Log in

Least squares estimators in measurement error models under the balanced loss function

  • Published:
Test Aims and scope Submit manuscript

Abstract

The ultrastructural form of the measurement error model is considered and a comparison of the direct and the reverse regression estimators is made under the balanced loss function, which explicitly takes into account the accuracy of the predictions and the precision of estimation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Cheng, C. and J.W. Van Ness (1999).Statistical Regression With Measurement Error. Arnold Publishers, London.

    MATH  Google Scholar 

  • Dolby, G.R. (1976). The ultrastructural relation: a synthesis of the functional and structural relation.Biometrika 63, 39–50.

    Article  MATH  MathSciNet  Google Scholar 

  • Fuller, W.A. (1987).Measurment Error Models. John Wiley, New York.

    Google Scholar 

  • Giles, J.A., D.E.A. Giles and K. Ohtani (1996). The exact risk of some pre-test and Stein-type regression estimates under balanced loss.Communications in Statistics—Theory and Methods 25, 2901–2924.

    MATH  MathSciNet  Google Scholar 

  • Gleser, L.J. (1992). The importance of assessing measurement reliabity in multivariate regression.Journal of the American Statistical Association 87, 696–707.

    Article  MATH  MathSciNet  Google Scholar 

  • Ohtani, K. (1998). The exact risk of a weighted average estimator of the OLS and Stein-rule estimators in regression under balanced loss.Statistics and Decisions,16, 35–45.

    MATH  MathSciNet  Google Scholar 

  • Schneeweiss, H. (1991). Note on a linear model with errors in the variables and with trend.Statistical Papers 32, 261–264.

    Article  MathSciNet  Google Scholar 

  • Srivastava, A.K. and Shalabh (1997). Asymptotic efficiency properties of least squares estimator in ultrastructural model.Test 6, 419–431.

    Article  MATH  MathSciNet  Google Scholar 

  • Wan, A.T.K. (1994). Risk comparison of the inequality constrained least squares and other related estimators under balanced loss.Economics Letters 46, 203–210.

    Article  MathSciNet  Google Scholar 

  • Zellner, A. (1994). Bayesian and Non-Bayesian estimation using balanced loss function. InStatistical Decision Theory and Related Topics V, 377–390 (S.S. Gupta and J.O. Berger eds.) Springer-Verlag.

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, Panjab University, 160 014, Chandigarh, India, India

    Shalabh

Authors
  1. Shalabh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shalabh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shalabh Least squares estimators in measurement error models under the balanced loss function. Test 10, 301–308 (2001). https://doi.org/10.1007/BF02595699

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02595699

Key Words

AMS subject classification

Search

Navigation