A COMPARISON OF REGRESSION METHODS FOR IMPROVED ESTIMATION IN SAMPLING

Authors

  • G.C. Misra Department of Statistics, D. A. V. College, Kanpur, India
  • A.K. Shukla Department of Statistics, D. A. V. College, Kanpur, India
  • S.K. Yadav Department of Statistics, D. A. V. College, Kanpur, India

Keywords:

nverse term model, linear regression estimator, Polynomial regression, simple random sampling.

Abstract

A linear model with an inverse term is proposed for estimation of population mean and population total in regression analysis. A comparison has been made in precisions of estimates of parameters, considering ordinary linear regression estimate, regression estimates using second degree polynomial for relationship between dependent variable ( y )and auxiliary variable ( x ) and also with estimates obtained in regression method of estimation incorporating inverse term model for describing the relationship between y and x. The gain in efficiency is also demonstrated with the help of a data set in which comparison of various estimates of regression method of estimation has also been made with simple random sampling.

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References

Bates, D.M. and Watts, D.G. (1988). Non-linear Regression Analysis and Its

Applications. John Wiley, New York.

Cochran, W.G. (1999). Sampling Techniques. John Wiley & Sons.

Des Raj. (1972). The Design of Sample Surveys. McGraw-Hill, New York.

Draper, N.R. and Smith, H. (1998). Applied Regression Analysis. John Wiley,

New York.

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Published

2009-12-01

How to Cite

Misra, G., Shukla, A., & Yadav, S. (2009). A COMPARISON OF REGRESSION METHODS FOR IMPROVED ESTIMATION IN SAMPLING. Journal of Reliability and Statistical Studies, 2(2), 85–90. Retrieved from https://journals.riverpublishers.com/index.php/JRSS/article/view/22073

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