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Matrix Variate Distribution Theory under Elliptical Models—V: The Non-Central Wishart and Inverted Wishart Distributions

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Abstract

The non-central Wishart and inverted Wishart distributions are studied in this work under elliptical models; some distributional results are based on some generalizations of the well-known Kummer relations, which leds us to determine that some moments have a polynomial representation. Then the non-central \(F\) and ‘‘studentized Wishart’’ distributions are derived in a general setting. After some generalizations, including the so called non-central generalized inverted Wishart distribution, the classical results based on Gaussian models are derived here as corollaries.

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REFERENCES

  1. M. S. Bartlett, On the theory of statistical regression, Proc. R. Soc. Edinb. 53, 260–283 (1933).

    Article  Google Scholar 

  2. F. J. Caro-Lopera, Invariant Polynomials of Hermitian Matrix Arguments and Applications, Master Thesis (Medellin, Universidad de Antioquia, 2002).

  3. F. J. Caro-Lopera, ‘‘The impossibility of a recurrence construction of the invariant polynomials by using the Laplace-Beltrami operator,’’ Far East Journal of Mathematical Sciences 100 (8), 1265–1288 (2016). https://doi.org/10.17654/MS100081265

    Article  MATH  Google Scholar 

  4. F. J. Caro-Lopera, J. A. Diaz-Garcia, and G. González-Farías, ‘‘Inference in statistical shape theory: elliptical configuration densities,’’ Journal of Statistical Research 43 (1), 1–19 (2009).

    MathSciNet  Google Scholar 

  5. F. J. Caro-Lopera, J. A. Diaz-Garcia, and G. González-Farías, Non-central elliptical configuration density, Journal of Multivariate Analysis 101 (1), 32–43 (2010).

    Article  MathSciNet  Google Scholar 

  6. F. J. Caro-Lopera, G. González-Farías, and N. Balakrishnan, On Generalized Wishart Distributions—I: Likelihood Ratio Test for Homogeneity of Covariance Matrices. Sankhya 76A (2), 179–194 (2014a).

  7. F. J. Caro-Lopera, G. González-Farías, and N. Balakrishnan, ‘‘On Generalized Wishart Distributions—II: Sphericity Test,’’ Sankhya 76A (2), 195–218 (2014b).

  8. F. J. Caro-Lopera, G. González-Farías, and N. Balakrishnan, ‘‘Matrix-variate distribution theory under elliptical models-4: Joint distribution of latent roots of covariance matrix and the largest and smallest latent roots,’’ Journal of Multivariate Analysis 145, 224–235 (2016).

  9. F. J. Caro-Lopera, G. González-Farías, and N. Balakrishnan, ‘‘Matrix-variate distribution theory under elliptical models: Likelihood ratio test for testinglocation and scale,’’ Sankhya B. Accepted (2018).

  10. Y. Chikuse, ‘‘Partial differential equations for hypergeometric functions of complex arguments matrices and their applications,’’ Ann. Inst. Statist. Math. 28, 187–199 (1976).

    Article  MathSciNet  Google Scholar 

  11. Y. Chikuse, ‘‘Methods for constructing top order invariant polynomials,’’ Econom. Theory. 3 (2), 195–207 (1987).

    Article  MathSciNet  Google Scholar 

  12. Y. Chikuse and A. Davis, ‘‘Some properties of invariant polynomials with matrix arguments and their applications in econometrics,’’ Ann. Inst. Statist. Math. 38, 109–122 (1986).

    Article  MathSciNet  Google Scholar 

  13. A. G. Constantine, ‘‘Non-central distribution problems in multivariate analysis,’’ The Annals of Mathematical Statistics 34, 1270–1285 (1963).

    Article  MathSciNet  Google Scholar 

  14. A. G. Constantine and R. J. Muirhead, ‘‘Asymptotic expansions for distributions of latent roots in multivariate analysis,’’ J. Multivariate Anal. 6, 369–391 (1976).

    Article  MathSciNet  Google Scholar 

  15. G. González-Farías and F. J. Caro-Lopera, ‘‘C-arrays: Partitions and related aspects,’’ in: Encyclopedia of Statistical Sciences, S. Kotz, N. Balakrishnan, C. B. Read, and B. Vidakovic, eds. (John Wiley and Sons, Hoboken, New Jersey, 2011). (Accepted).

  16. A. W. Davis, ‘‘On the construction of a class of invariant polynomials in several matrices, extending the zonal polynomials,’’ Ann. Inst. Statist. Math. 33, 297–313 (1981).

    Article  MathSciNet  Google Scholar 

  17. A. W. Davis, Invariant Polynomials with Two Matrix Arguments, Extending the Zonal Polynomials, in: Multivariate Analysis V, P. R. Krishnaiah, ed. (Amsterdam: North-Holland, 1980), pp. 287–299.

    Google Scholar 

  18. A. W. Davis, ‘‘Invariant polynomials with two matrix arguments extending the zonal polynomials: Applications to multivariate distribution theory,’’ Ann. Inst. Statist. Math. 31 (A), 465–485 (1979).

  19. A. W. Davis, Polynomials of Matrix Arguments, in: Encyclopedia of Statistical Sciences, S. Kotz, N. Balakrishnan, C. B. Read, and B. Vidakovic, eds. (Hoboken: Wiley, 2006).

    Google Scholar 

  20. A. W. Davis and J. B. F. Field, ‘‘Tables of some multivariate test criteria,’’ Tech. Rept. No. 32, Division of Mathematical Statistics, C.S.I.R.O., Canberra, Australia (1971).

    Google Scholar 

  21. J. A. Diaz-Garcia, ‘‘Integral Properties of Zonal Spherical Functions, Hypergeometric Functions, and Invariant Polynomials,’’ Journal of the Iranian Statistical Society 13 (1), 83–124 (2014).

    MathSciNet  MATH  Google Scholar 

  22. J. A. Diaz-Garcia and F. J. Caro-Lopera, ‘‘An alternative approach for deriving the Laplace–Beltrami operador for the zonal polynomials of positive semidefinite and definite matrix argument,’’ Far East Journal of Mathematical Sciences 22 (3), 273–290 (2006).

    MATH  Google Scholar 

  23. J. A. Diaz-Garcia and F. J. Caro-Lopera, ‘‘Derivation of the Laplace–Beltrami operador for the zonal polynomials of positive definite hermitian matrix argument,’’ Applied Mathematical Sciences 1 (4), 191–200 (2007).

    MathSciNet  MATH  Google Scholar 

  24. J. A. Diaz-Garcia and F. J. Caro-Lopera, ‘‘Matrix generalised Kummer relation,’’ South African Statistical Journal 49 (1) (2015).

  25. I. Dumitriu, A. Edelman, and G. Shuman, ‘‘MOPS: Multivariate orthogonal polynomials (symbolically),’’ Journal of Symbolic Computation 42 (6), 587–620 (2007).

    Article  MathSciNet  Google Scholar 

  26. Statistical Inference in Elliptically Contoured and Related Distributions, K. T. Fang and T. W. Anderson, eds., (Allerton Press, New York, 1990).

    MATH  Google Scholar 

  27. K. T. Fang and Y. T. Zhang, Generalized Multivariate Analysis (Science Press, Springer-Verlag, Beijing, 1990).

    MATH  Google Scholar 

  28. A.K. Gupta and D. K. Nagar, Matrix Variate Distributions (Chapman and Hall, Boca Raton, 1999).

    MATH  Google Scholar 

  29. C. S. Herz, ‘‘Bessel functions of matrix argument,’’ Ann. Math. 61, 474–523 (1955).

    Article  MathSciNet  Google Scholar 

  30. H. Jack, ‘‘A class of symmetric polynomials with a parameter,’’ Proc. Roy. Soc. Edinburgh Sect A. 69, 1-17 (1970).

    MathSciNet  MATH  Google Scholar 

  31. A. T. James, ‘‘Calculation of zonal polynomial coefficients by use of the Laplace–Beltrami operator,’’ The Annals of Mathematical Statistics 39, 1711–1718 (1968).

    Article  MathSciNet  Google Scholar 

  32. A. T. James, ‘‘Distributions of matrix variates and latent roots derived from normal samples,’’ Ann. Math. Statist. 39, 1711–1718 (1964).

    Article  MathSciNet  Google Scholar 

  33. A. T. James, ‘‘The distribution of the latent roots of the covariance matrix,’’ Ann. Math. Statist. 31, 151–158 (1960).

    Article  MathSciNet  Google Scholar 

  34. C. G. Khatri, ‘‘On certain distribution problems based on positive definite quadratic functions in normal vectors,’’ The Annals of Mathematical Statistics 37, 468–479 (1966).

    Article  MathSciNet  Google Scholar 

  35. C. G. Khatri, ‘‘On the exact finite series distribution of the smallest or the largest root of matrices in three situations,’’ J. Multivariate Analysis 2, 201–207 (1972).

    Article  MathSciNet  Google Scholar 

  36. P. Koev and A. Edelman, ‘‘The efficient evaluation of the hypergeometric function of a matrix argument,’’ Math. Comp. 75, 833–846 (2006).

    Article  MathSciNet  Google Scholar 

  37. L. Lapointe and L. Vinet, ‘‘A Rodrigues formula for the Jack polynomials and the Macdonald–Stanley conjecture,’’ International Mathematics Research Notices 9 (1), 419–424 (1995).

    Article  MathSciNet  Google Scholar 

  38. F. Li and Y. Xue, ‘‘Zonal polynomials and hypergeometric functions of quaternion matrix argument,’’ Communications in Statistics: Theory and Methods 38 (8), 1184–1206 (2009).

    Article  MathSciNet  Google Scholar 

  39. R. Li, ‘‘The expected values of invariant polynomials with matrix argument of elliptical distributions,’’ Acta Mathematicae Applicatae Sinica 13 (1), 64–70 (1997).

    Article  MathSciNet  Google Scholar 

  40. J. R. Magnus, Linear Structures (Charles Griffin and Company Ltd, London, 1988).

    MATH  Google Scholar 

  41. R. J. Muirhead, Aspects of Multivariate Statistical Theory, Wiley Series in Probability and Mathematical Statistics (John Wiley and Sons, Inc., New York, 2005).

  42. B. N. Nagarsenker and K. C. S. Pillai, ‘‘Distribution of the likelihood ratio criterion for testing a hypothesis specifying a covariance matrix,’’ Biometrika 60, 359–394 (1973).

    Article  MathSciNet  Google Scholar 

  43. B. N. Nagarsenker and K. C. S. Pillai, ‘‘Distribution of the likelihood ratio criterion for testing \(\Sigma=\Sigma_{0}\), \(\mu=\mu_{0}\),’’ J. Multi. Analysis 4, 114–122 (1974).

    Article  MathSciNet  Google Scholar 

  44. R. Khattree and R. D. Gupta, ‘‘Estimation of matrix valued realized signal to noise ratio,’’ Journal of Multivariate Analysis 30 (2), 312–327 (1989).

    Article  MathSciNet  Google Scholar 

  45. R. Khattree and R. D. Gupta, ‘‘Some probability distributions connected with beta and gamma matrices,’’ Communications in Statistics—Theory and Methods 21 (2) (1992).

  46. R. P. Stanley, ‘‘Some combinatorial properties of Jack symmetric functions,’’ Adv. Math. 77, 76–115 (1989).

    Article  MathSciNet  Google Scholar 

  47. N. Sugiura, ‘‘Derivatives of the characteristic root of a symmetric or a Hermitian matrix with two applications in multivariate analysis,’’ Commun. Statist. 1, 393–417 (1973).

    MathSciNet  MATH  Google Scholar 

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ACKNOWLEDGMENT

This research work was supported by a joint project between the University of Medellin (Medellin, Colombia) and the Center for Mathematical Research (Guanajuato, Mexico). We are greatly thankful with the Editor and the Referees of this paper. Their carefully revisions and comments provided important improvement of the final version of this article.

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Correspondence to Francisco J. Caro-Lopera, Graciela González Farías or N. Balakrishnan.

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Caro-Lopera, F.J., González Farías, G. & Balakrishnan, N. Matrix Variate Distribution Theory under Elliptical Models—V: The Non-Central Wishart and Inverted Wishart Distributions. Math. Meth. Stat. 31, 18–42 (2022). https://doi.org/10.3103/S1066530722010021

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