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Research Article Open access CC BY 4.0

An Alternative Estimator for the Estimation of Polynomial Regression Model (PRM)

David I. Oladapo, Eli Yaovi Ametepey, Victor O. Akinsola, Folake A. Amao, Samuel B. Atoyebi

Journal of Advances in Mathematics and Computer Science · pp. 1–11 · Published 30 Mar 2023

10.9734/jamcs/2023/v38i71768

Abstract

The maximum likelihood (ML) approach is used to fit the polynomial regression model (PRM) in the presence of small sample sizes. The ML technique is applied to the data of PPP, GDP, and output/total production cost in Nigeria between 1989 and 1999. The results of the analyses (by ML approach and that of the OLS) are presented for comparison. The analysis shows that the ML gives parameter estimates of 128.889, -5.24, -29.208, 10.523 and the OLS resulted in 128.009, 5.196, -30.376, 11.009. The analysis of the first data set (of iron content and weight loss of some specimen tested in a corrosion wheel set-up) shows that both estimators accounted for good fit because they both have high R2 values and significant t-ratios. The result of the model fit of the four data sets using ML results in reasonable parameter estimates (with lesser S.E relative to the parameter estimates), lower MSE, and very high R2-values. Although both methods were generally well adapted, ML was more effective than OLS because it led to a smaller sample size's MSE.

Polynomial regression model (PRM) Maximum Likelihood (ML) Ordinary Least Squares (OLS) Mean square error (MSE) Unbiasedness Robustness; Efficiency

References (17)

  1. 1 Applied Regression Analysis [DOI]
  2. 2 Basic Econometrics, 4th Edition
  3. 3 On the Use of Polynomial Regression Equations As An Alternative to Difference Scores in Organizational Research [DOI]
  4. 4 Modelling using Polynomial Regression [DOI]
  5. 5 Elements of Econometrics [DOI]
  6. 6 Statistical Regression with Measurement Error [DOI]
  7. 7 Estimation of Nonlinear Models with Measurement Error [DOI]
  8. 8 Polynomial Regression and Estimating Functions in the Presence of Multiplicative Measurement Error [DOI]
  9. 9 The application of the method of least squares to the interpolation of sequences [DOI]
  10. 10 Polynomial Regression With Errors in the Variables [DOI]
  11. 11 Polynomial Regression Model of Making Cost Prediction In Mixed Cost Analysis
  12. 12 Use of Regression Functions for Improved Estimation of Means [DOI]
  13. 13 An Improved Volumetric Estimation Using Polynomial Regression
  14. 14 On An Alternative Method Of Estimation Of Polynomial Regression Models
  15. 15 A Spreadsheet Model for Teaching Regression Analysis [DOI]
  16. 16 Australian Journal of Basic and Applied Sciences [DOI]
  17. 17 Alternatives to Difference Scores: Polynomial Regression and Response Surface Methodology [DOI]

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