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Estimation of missing data in analysis of covariance: A least-squares approach

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  • Chibueze E. Ogbonnaya
  • Emeka C. Uzochukwu

Abstract

A method is proposed for the estimation of missing data in analysis of covariance models. This is based on obtaining an estimate of the missing observation that minimizes the error sum of squares. Specific derivation of this estimate is carried out for the one-factor analysis of covariance, and numerical examples are given to show the nature of the estimates produced. Parameter estimates of the imputed data are then compared with those of the incomplete data.

Suggested Citation

  • Chibueze E. Ogbonnaya & Emeka C. Uzochukwu, 2016. "Estimation of missing data in analysis of covariance: A least-squares approach," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(7), pages 1902-1909, April.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:7:p:1902-1909
    DOI: 10.1080/03610926.2013.868000
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