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Variance components for two-way nested design data

  • Received : 2018.01.10
  • Accepted : 2018.04.07
  • Published : 2018.05.31

Abstract

This paper discusses the use of projections for the sums of squares in the analyses of variance for two-way nested design data. The model for this data is assumed to only have random effects. Two different sizes of experimental units are required for a given experimental situation, since nesting is assumed to occur both in the treatment structure and in the design structure. So, variance components are coming from the sources of random effects of treatment factors and error terms in different sizes of experimental units. The model for this type of experimental situation is a random effects model with more than one error terms and therefore estimation of variance components are concerned. A projection method is used for the calculation of sums of squares due to random components. Squared distances of projections instead of using the usual reductions in sums of squares that show how to use projections to estimate the variance components associated with the random components in the assumed model. Expectations of quadratic forms are obtained by the Hartley's synthesis as a means of calculation.

Keywords

References

  1. Graybill FA (1976). Theory and Application of the Linear Model, Wadsworth, California.
  2. Hartley HO (1967). Expectations, variances and covariances of ANOVA mean squares by 'synthesis', Biometrics, 23, 105-114. https://doi.org/10.2307/2528284
  3. Henderson CR (1953). Estimation of variance and covariance components, Biometrics, 9, 226-252. https://doi.org/10.2307/3001853
  4. Khan AR, Saleem SMA, and Mehdi H (2017). Detection of edges using two-way nested design, International Journal of Advanced Computer Science and Applications, 8, 136-144.
  5. Milliken GA and Johnson DE (1984). Analysis of Messy Data Volume I: Designed Experiments, Van Nostrand Reinhold, New York.
  6. Montgomery DC (1976). Design and Analysis of Experiments, John Wiley & Sons, New York.
  7. Searle SR (1971). Linear Models, John Wiley & Sons, New York.
  8. Sharma HL (2014). Nested balanced n-ary designs and their PB arrays, Journal of Reliability and Statistical Studies, 7, 29-36.