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Congruent Families and Invariant Tensors

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Information Geometry and Its Applications (IGAIA IV 2016)

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Abstract

A classical result of Chentsov states that – up to constant multiples – the only 2-tensor field of a statistical model which is invariant under congruent Markov morphisms is the Fisher metric and the only invariant 3-tensor field is the Amari–Chentsov tensor. We generalize this result for arbitrary degree n, showing that any family of n-tensors which is invariant under congruent Markov morphisms is algebraically generated by the canonical tensor fields defined in Ay, Jost, Lê, Schwachhöfer (Bernoulli, 24:1692–1725, 2018, [4]).

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Notes

  1. 1.

    Since we do not consider non-covariant n-tensor fields in this paper, we shall suppress the attribute covariant.

References

  1. Amari, S.: Theory of information spaces. A geometrical foundation of statistics. POST RAAG Report 106 (1980)

    Google Scholar 

  2. Amari, S.: Differential geometry of curved exponential families curvature and information loss. Ann. Stat. 10, 357–385 (1982)

    Article  MathSciNet  Google Scholar 

  3. Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Information geometry and sufficient statistics. Probab. Theory Relat. Fields 162, 327–364 (2015)

    Article  MathSciNet  Google Scholar 

  4. Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Parametrized measure models. Bernoulli 24(3), 1692–1725 (2018)

    Article  MathSciNet  Google Scholar 

  5. Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Information Geometry, Ergebnisse der Mathematik und ihrer Grenzgebiete. Springer, Berlin (2017)

    Google Scholar 

  6. Bauer, M., Bruveris, M., Michor, P.: Uniqueness of the Fisher-Rao metric on the space of smooth densities. Bull. Lond. Math. Soc. 48(3), 499–506 (2016)

    Article  MathSciNet  Google Scholar 

  7. Bauer, M., Bruveris, M., Michor, P. In: Presentation at the Fourth Conference on Information Geometry and its Applications. (IGAIA IV, 2016), Liblice, Czech Republic

    Google Scholar 

  8. Campbell, L.L.: An extended Chentsov characterization of a Riemannian metric. Proc. Am. Math. Soc. 98, 135–141 (1986)

    MATH  Google Scholar 

  9. Chentsov, N.: Category of mathematical statistics. Dokl. Acad. Nauk. USSR 164, 511–514 (1965)

    Google Scholar 

  10. Chentsov, N.: Algebraic foundation of mathematical statistics. Math. Operationsforsch. Stat. Ser. Stat. 9, 267–276 (1978)

    MathSciNet  MATH  Google Scholar 

  11. Chentsov, N.: Statistical Decision Rules and Optimal Inference. Moscow, Nauka (1972) (in Russian); English translation in: Translation of Mathematical Monograph, vol. 53. American Mathematical Society, Providence, (1982)

    Google Scholar 

  12. Dowty, J.: Chentsov’s theorem for exponential families. arXiv:1701.08895

  13. Efron, B.: Defining the curvature of a statistical problem (with applications to second order efficiency), with a discussion by Rao, C.R., Pierce, D.A., Cox, D.R., Lindley, D.V., LeCam, L., Ghosh, J.K., Pfanzagl, J., Keiding, N., Dawid, A.P., Reeds, J., and with a reply by the author. Ann. Statist. 3, 1189–1242 (1975)

    Article  MathSciNet  Google Scholar 

  14. Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proc. R. Soc. Lond. Ser. A. 186, 453–461 (1946)

    Article  MathSciNet  Google Scholar 

  15. Jost, J., Lê, H.V., Schwachhöfer, L.: The Cramér-Rao inequality on singular statistical models I. arXiv:1703.09403 (2017)

  16. Lê, H.V.: The uniqueness of the Fisher metric as information metric. Ann. Inst. Stat. Math. 69, 879–896 (2017)

    Article  MathSciNet  Google Scholar 

  17. Morozova, E., Chentsov, N.: Natural geometry on families of probability laws, Itogi Nauki i Techniki, Current problems of mathematics, Fundamental directions, vol. 83, pp. 133–265. Moscow (1991)

    Google Scholar 

  18. Rao, C.R.: Information and the accuracy attainable in the estimation of statistical parameters. Bull. Calcutta Math. Soc. 37, 81–89 (1945)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was mainly carried out at the Max Planck Institute for Mathematics in the Sciences in Leipzig, and we are grateful for the excellent working conditions provided at that institution. The research of H.V. Lê was supported by the GAČR project 18-01953J and RVO: 67985840. L. Schwachhöfer acknowledges partial support by grant SCHW893/5-1 of the Deutsche Forschungsgemeinschaft.

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Correspondence to Lorenz Schwachhöfer .

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Schwachhöfer, L., Ay, N., Jost, J., Vân Lê, H. (2018). Congruent Families and Invariant Tensors. In: Ay, N., Gibilisco, P., Matúš, F. (eds) Information Geometry and Its Applications . IGAIA IV 2016. Springer Proceedings in Mathematics & Statistics, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-319-97798-0_6

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