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On Some Results of C. Radhakrishna Rao Applicable to the Analysis of Multi-Environment Variety Trials

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Abstract

The analysis of results of a series of experiments repeated at several environments with the same set of plant varieties (genotypes) is usually based on a mixed effects model. Because of possible different responses of the varieties to variable environmental conditions, the standard mixed model for that analysis becomes questionable. Therefore, a more general mixed model is to be considered. However, in its most general form it involves usually a large number of variance and covariance components to be estimated. This causes computational problems, even when using advanced algorithms, unless some simplifying structures are imposed on the general covariance matrix. It has appeared, that these problems can be avoided when adopting a classic method proposed by Rao (1972). This method has been explored recently by Calinski, Czajka, Kaczmarek, Krajewski, and Pilarczyk (2005). The purpose of the present paper is to show the use of that and some other theoretical results of C. Radhakrishna Rao in detail.

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References

  • Caliński, T., Czajka, S., Kaczmarek, Z., Krajewski, P., Pilarczyk, W., 2005. Analyzing multi-environment variety trials using randomization-derived mixed models. Biometrics, 61, 448–455.

    Article  MathSciNet  Google Scholar 

  • Demidenko, E., 2004. Mixed Models: Theory and Applications. Wiley, Hoboken, New Jersey.

    Book  Google Scholar 

  • Denis, J.-B., Piepho, H.-P., van Eeuwijk, F. A., 1997. Modelling expectation and variance for genotype by environment data. Heredity, 79, 162–171.

    Article  Google Scholar 

  • Gilmour, A. R., Thompson, R., Cullis, B. R., 1995. Average information REML: An efficient algorithm for variance parameter estimation in linear mixed models. Biometrics, 51, 1440–1450.

    Article  Google Scholar 

  • Gogel, B. J., Cullis, B. R., Verbyla, A. P., 1995. REML estimation of multiplicative effects in multi-environment variety trials. Biometrics, 51, 744–749.

    Article  Google Scholar 

  • Nair, K. R., 1944. The recovery of inter-block information in incomplete block designs. Sankhyā, 6, 383–390.

    MathSciNet  MATH  Google Scholar 

  • Nelder, J. A., 1954. The interpretation of negative components of variance. Biometrika, 41, 544–548.

    Article  MathSciNet  Google Scholar 

  • Nelder, J. A., 1965. The analysis of randomized experiments with orthogonal block structure. Proceedings of the Royal Society, Series A, 283, 147–178.

    Article  Google Scholar 

  • Nelder, J. A., 1968. The combination of information in generally balanced designs. Journal of the Royal Statistical Society, Series B, 30, 303–311.

    Article  MathSciNet  Google Scholar 

  • Patterson, H. D., Silvey, V., 1980. Statutory and recommended list trials of crop varieties in the United Kingdom (with discussion). Journal of the Royal Statistical Society, Series A, 143, 219–252.

    Article  Google Scholar 

  • Patterson, H. D., Thompson, R., 1975. Maximum likelihood estimation of components of variance. In Corsten, L. C. A. and Postelnicu, T. (Eds.), Proceedings of the 8th International Biometric Conference, 197–207. Editura Academiei, Bucureşti.

    Google Scholar 

  • Patterson, H. D., Williams, E. R., 1976. A new class of resolvable incomplete block designs. Biometrika, 63, 83–92.

    Article  MathSciNet  Google Scholar 

  • Rao, C. R., 1947. General methods of analysis for incomplete block designs. Journal of the American Statistical Association, 42, 541–561.

    Article  MathSciNet  Google Scholar 

  • Rao, C. R., 1956. On the recovery of inter-block information in varietal trials. Sankhyā, 17, 105–114.

    MathSciNet  MATH  Google Scholar 

  • Rao, C. R., 1959. Expected values of mean squares in the analysis of incomplete block experiments and some comments based on them. Sankhyā, 21, 327–336.

    MathSciNet  MATH  Google Scholar 

  • Rao, C. R., 1970. Estimation of heteroscedastic variances in linear models. Journal of the American Statistical Association, 65, 161–172.

    Article  MathSciNet  Google Scholar 

  • Rao, C. R., 1971. Estimation of variance and covariance components—MINQUE theory. Journal of Multivariate Analysis, 1, 257–275.

    Article  MathSciNet  Google Scholar 

  • Rao, C. R., 1972. Estimation of variance and covariance components in linear models. Journal of the American Statistical Association, 67, 112–115.

    Article  MathSciNet  Google Scholar 

  • Rao, C. R., 1973. Representations of best linear unbiased estimators in the Gauss-Markoff model with a singular dispersion matrix. Journal of Multivariate Analysis, 3, 276–292.

    Article  MathSciNet  Google Scholar 

  • Rao, C. R., 1979. MINQUE theory and its relation to ML and MML estimation of variance components. Sankhyā, Series B, 41, 138–153.

    MathSciNet  MATH  Google Scholar 

  • Rao, C.R. and Kleffe, J., 1988. Estimation of Variance Components and Applications. North-Holland, Amsterdam.

    MATH  Google Scholar 

  • Rao, C.R. and Toutenburg, H., 1999. Linear Models: Least Squares and Alternatives. Springer, New York.

    MATH  Google Scholar 

  • Smith, A., Cullis, B., Gilmour, A., 2001a. The analysis of crop variety evaluation data in Australia. Australian and New Zealand Journal of Statistics, 43, 129–145.

    Article  MathSciNet  Google Scholar 

  • Smith, A., Cullis, B., Thompson, R., 2001b. Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics, 57, 1138–1147.

    Article  MathSciNet  Google Scholar 

  • White, R. F., 1975. Randomization in the analysis of variance. Biometrics, 31, 555–571.

    Article  MathSciNet  Google Scholar 

  • Yates, F., 1936a. Incomplete randomized blocks. Annals of Eugenics, 7, 121–140.

    Article  Google Scholar 

  • Yates, F., 1936b. A new method of arranging variety trials involving a large number of varieties. Journal of Agricultural Science, 26, 424–455.

    Article  Google Scholar 

  • Yates, F., 1939. The recovery of inter-block information in variety trials arranged in three-dimensional lattices. Annals of Eugenics, 9, 136–156.

    Article  Google Scholar 

  • Yates, F., 1940a. Lattice squares. Journal of Agricultural Science, 30, 672–687.

    Article  Google Scholar 

  • Yates, F., 1940b. The recovery of inter-block information in balanced incomplete block designs. Annals of Eugenics, 10, 317–325.

    Article  Google Scholar 

  • Yates, F., Cochran, W. G., 1938. The analysis of groups of experiments. Journal of Agricultural Science, 28, 556–580.

    Article  Google Scholar 

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Correspondence to Tadeusz Caliński.

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Caliński, T. On Some Results of C. Radhakrishna Rao Applicable to the Analysis of Multi-Environment Variety Trials. J Stat Theory Pract 1, 347–356 (2007). https://doi.org/10.1080/15598608.2007.10411845

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  • DOI: https://doi.org/10.1080/15598608.2007.10411845

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