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]).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Since we do not consider non-covariant n-tensor fields in this paper, we shall suppress the attribute covariant.
References
Amari, S.: Theory of information spaces. A geometrical foundation of statistics. POST RAAG Report 106 (1980)
Amari, S.: Differential geometry of curved exponential families curvature and information loss. Ann. Stat. 10, 357–385 (1982)
Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Information geometry and sufficient statistics. Probab. Theory Relat. Fields 162, 327–364 (2015)
Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Parametrized measure models. Bernoulli 24(3), 1692–1725 (2018)
Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Information Geometry, Ergebnisse der Mathematik und ihrer Grenzgebiete. Springer, Berlin (2017)
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)
Bauer, M., Bruveris, M., Michor, P. In: Presentation at the Fourth Conference on Information Geometry and its Applications. (IGAIA IV, 2016), Liblice, Czech Republic
Campbell, L.L.: An extended Chentsov characterization of a Riemannian metric. Proc. Am. Math. Soc. 98, 135–141 (1986)
Chentsov, N.: Category of mathematical statistics. Dokl. Acad. Nauk. USSR 164, 511–514 (1965)
Chentsov, N.: Algebraic foundation of mathematical statistics. Math. Operationsforsch. Stat. Ser. Stat. 9, 267–276 (1978)
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)
Dowty, J.: Chentsov’s theorem for exponential families. arXiv:1701.08895
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)
Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proc. R. Soc. Lond. Ser. A. 186, 453–461 (1946)
Jost, J., Lê, H.V., Schwachhöfer, L.: The Cramér-Rao inequality on singular statistical models I. arXiv:1703.09403 (2017)
Lê, H.V.: The uniqueness of the Fisher metric as information metric. Ann. Inst. Stat. Math. 69, 879–896 (2017)
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)
Rao, C.R.: Information and the accuracy attainable in the estimation of statistical parameters. Bull. Calcutta Math. Soc. 37, 81–89 (1945)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-97798-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97797-3
Online ISBN: 978-3-319-97798-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)