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Operations with Iso-structured Models with Commutative Orthogonal Block Structure: An Introductory Approach

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Statistical Modelling and Risk Analysis (ICRA 2022)

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Abstract

An approach to models based on an algebraic context allows interesting and useful statistical results to be derived or at least better understood. In the approach to models with commutative orthogonal block structure via algebraic structure it is possible to show that the orthogonal projection matrix in the space spanned by the mean vector commuting with the covariance matrix guarantees least squares estimators giving best linear unbiased estimators for estimable vectors. In this work we focus on the possibility of performing operations with models with commutative orthogonal block structure that are iso-structured, that is, models generating the same commutative Jordan Algebra of symmetric matrices.

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References

  1. Caliński, T., Kageyama, S.: Block Designs: A Randomization Approach. Vol. I: Analysis, Lecture Note in Statistics, vol. 150. Springer, New York (2000)

    Google Scholar 

  2. Caliński, T., Kageyama, S.: Block Designs: A Randomization Approach. Vol. II: Design, Lecture Note in Statistics, vol. 170. Springer, New York (2003)

    Google Scholar 

  3. Carvalho, F., Mexia, J.T, Oliveira, M.: Canonic inference and commutative orthogonal block structure. Discuss. Math. Probab. Stat. 28(2), 171–181 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Carvalho, F., Mexia, J.T, Oliveira, M.: Estimation in models with commutative orthogonal block structure. J. Stat. Theory Practice 3(2), 525–535 (2009). https://doi.org/10.1080/15598608.2009.10411942

    Article  MathSciNet  MATH  Google Scholar 

  5. Carvalho, F., Mexia, J.T, Santos, C.: Commutative orthogonal block structure and error orthogonal models. Electron. J. Linear Algebra 25, 119–128 (2013). https://doi.org/10.13001/1081-3810.1601

    MathSciNet  MATH  Google Scholar 

  6. Carvalho, F., Mexia, J.T, Santos, C., Nunes, C.: Inference for types and structured families of commutative orthogonal block structures. Metrika 78, 337–372 (2015). https://doi.org/10.1007/s00184-014-0506-8

    Article  MathSciNet  MATH  Google Scholar 

  7. Ferreira, S., Ferreira, D., Nunes, C., Carvalho, F., Mexia, J.T.: Orthogonal block structure and uniformly best linear unbiased estimators. In: Ahmed, S., Carvalho, F., Puntanen, S. (eds.) Matrices, Statistics and Big Data. IWMS 2016. Contributions to Statistics. Springer (2019)

    Google Scholar 

  8. Fonseca, M., Mexia, J. T., Zmyślony,R.: Binary operations on Jordan algebras and orthogonal normal models. Linear Algebra Appl. 117(1), 75–86 (2006). https://doi.org/10.1016/j.laa.2006.03.045

    Article  MathSciNet  MATH  Google Scholar 

  9. Fonseca, M., Mexia, J.T., Zmyślony, R.: Inference in normal models with commutative orthogonal block structure. Acta Comment. Univ. Tartu. Math. 12, 3–16 (2008)

    MathSciNet  MATH  Google Scholar 

  10. Gusmão, L., Mexia, J.T., d Gomes, M.L.: Mapping of equipotential zones for cultivar yield pattern evolution. Plant Breed. 103, 293–298 (1989)

    Google Scholar 

  11. Jordan, P., Von Neumann, J., Wigner, E.: On an algebraic generalization of the quantum mechanical formulation. Ann. Math. 35(1), 29–64 (1934). https://doi.org/10.2307/1968117

    Article  MathSciNet  MATH  Google Scholar 

  12. Malley, J.D.: Statistical Applications of Jordan Algebras. Lecture Notes in Statistics, vol. 91. Springer, New York (1994)

    Google Scholar 

  13. Mexia, J.T., Nunes, C., Santos, C.: Structured families of normal models with COBS. In: 17th International Workshop in Matrices and Statistics, 23–26 July, Tomar (Portugal), Conference paper (2008)

    Google Scholar 

  14. Mexia, J.T., Vaquinhas, R., Fonseca, M., Zmyślony, R.: COBS: segregation, matching, crossing and nesting. In: Latest Trends and Applied Mathematics, Simulation, Modelling, 4th International Conference on Applied Mathematics, Simulation, Modelling, ASM’10, pp. 249–255 (2010)

    Google Scholar 

  15. Michalski, A., Zmyślony, R.: Testing hypothesis for variance components in mixed linear models. Statistics 27, 297–310 (1996). https://doi.org/10.1080/02331889708802533

    Article  MathSciNet  MATH  Google Scholar 

  16. Michalski, A., Zmyślony, R.: Testing hypothesis for linear functions of parameters in mixed linear models. Tatra Mountain Math. Publ. 17, 103–110 (1999)

    MATH  Google Scholar 

  17. Nelder, J.A.: The analysis of randomized experiments with orthogonal block structure I, Block structure and the null analysis of variance. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 283, 147–162 (1965)

    MATH  Google Scholar 

  18. Nelder, J.A.: The analysis of randomized experiments with orthogonal block structure II. Treatment structure and the general analysis of variance. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 283, 163–178 (1965)

    MATH  Google Scholar 

  19. Nunes, C., Santos, C., Mexia, J.T.: Relevant statistics for models with commutative orthogonal block structure and unbiased estimator for variance components. J. Interdiscip. Math. 11, 553–564 (2008). https://doi.org/10.1080/09720502.2008.10700581

    Article  MathSciNet  MATH  Google Scholar 

  20. Ramos, P., Fernandes, C., Mexia, J.T.: Algebraic structure for interaction on mixed models. J. Interdiscip. Math. 18(1–2), 43–52. https://doi.org/10.1080/09720510.2014.927622

  21. Rao, C., Rao, M.: Matrix Algebras and Its Applications to Statistics and Econometrics. World Scientific (1998)

    Google Scholar 

  22. Santos, C., Nunes, C., Mexia, J.T.: OBS, COBS and mixed models associated to commutative Jordan Algebra. Bulletin of the ISI, LXII, Proceedings of 56th session of the International Statistical Institute, Lisbon, pp. 3271–3274 (2008)

    Google Scholar 

  23. Santos, C., Nunes, C., Dias, C., Mexia, J.T.: Joining models with commutative orthogonal block structure. Linear Algebra Its Appl. 517, 235–245 (2017). https://doi.org/10.1016/j.laa.2016.12.019

    Article  MathSciNet  MATH  Google Scholar 

  24. Santos, C., Dias, C., Nunes, C., Mexia, J.T.: On the derivation of complex linear models from simpler ones. In: Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management Detroit, Michigan, USA, August 10–14, 2020, pp. 603–613 (2020)

    Google Scholar 

  25. Seely, J.: Linear spaces and unbiased estimation. Ann. Math. Stat. 41(5), 1725–1734 (1970). https://doi.org/10.1214/aoms/1177696818

    Article  MathSciNet  MATH  Google Scholar 

  26. Seely, J.: Quadratic subspaces and completeness. Ann. Math. Stat. 42(2), 710–721 (1971). https://doi.org/10.1214/aoms/1177693420

    Article  MathSciNet  MATH  Google Scholar 

  27. Seely, J.: Minimal sufficient statistics and completeness for multivariate normal families. Sankhya 39, 170–185 (1977)

    MathSciNet  MATH  Google Scholar 

  28. Seely, J., Zyskind, G.: Linear Spaces and minimum variance estimation. Ann. Math. Stat. 42(2), 691–703 (1971). https://doi.org/10.1214/aoms/1177693418

    Article  MathSciNet  MATH  Google Scholar 

  29. Vanleeuwen, D., Seely, J., Birkes, D.: Sufficient conditions for orthogonal designs in mixed linear models. J. Stat. Plan. Inference 73, 373–389 (1998). https://doi.org/10.1016/S0378-3758(98)00071-8

    Article  MathSciNet  MATH  Google Scholar 

  30. Vanleeuwen, D., Birkes, D., Seely, J.: Balance and orthogonality in designs for mixed classification models. Ann. Stat. 27(6), 1927–1947 (1999). https://doi.org/10.1214/aos/1017939245

    MathSciNet  MATH  Google Scholar 

  31. Zmyślony, R.: A characterization of best linear unbiased estimators in the general line-ar model. In: Lecture Notes in Statistics, vol. 2, pp. 365–373 (1978)

    Google Scholar 

  32. Zymślony, R., Drygas, H.: Jordan algebras and Bayesian quadratic estimation of variance components. Linear Algebra Appl. 168, 259–275 (1992). https://doi.org/10.1016/0024-3795(92)90297-N

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was partially supported by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) through the projects UIDB/00297/2020 and UIDB/00212/2020.

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Correspondence to Carla Santos .

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Santos, C., Dias, C., Nunes, C., Mexia, J.T. (2023). Operations with Iso-structured Models with Commutative Orthogonal Block Structure: An Introductory Approach. In: Kitsos, C.P., Oliveira, T.A., Pierri, F., Restaino, M. (eds) Statistical Modelling and Risk Analysis. ICRA 2022. Springer Proceedings in Mathematics & Statistics, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-031-39864-3_13

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