Abstract
Let G be the graph corresponding to the graphical model of nearest neighbor interaction in a Gaussian character. We study Natural Exponential Families (NEF) of Wishart distributions on convex cones \(Q_G\) and \(P_G\), where \(P_G\) is the cone of tridiagonal positive definite real symmetric matrices, and \(Q_G\) is the dual cone of \(P_G\). The Wishart NEF that we construct include Wishart distributions considered earlier for models based on decomposable(chordal) graphs. Our approach is, however, different and allows us to study the basic objects of Wishart NEF on the cones \(Q_G\) and \(P_G\). We determine Riesz measures generating Wishart exponential families on \(Q_G\) and \(P_G\), and we give the quadratic construction of these Riesz measures and exponential families. The mean, inverse-mean, covariance and variance functions, as well as moments of higher order, are studied and their explicit formulas are given.
Similar content being viewed by others
References
Amari, S., Nagaoka, H. (2007). Methods of information geometry. Translations of mathematical monographs (Vol. 191). New York: American Mathematical Society.
Andersson, S. A., Klein, T. (2010). On Riesz and Wishart distributions associated with decomposable undirected graphs. Journal of Multivariate Analysis, 101(4), 789–810.
Bondy, J. A., Murty, U. S. (2008). Graph theory. Graduate texts in mathematics (Vol. 244). London: Springer.
Brown, L. D. (1986). Fundamentals of statistical exponential families with applications in statistical decision theory. Lecture Notes-Monograph Series, 9. Hayward, California: Institute of Mathematical Statistics.
Casalis, M., Letac, G. (1996). The Lukacs–Olkin–Rubin characterization of Wishart distributions on symmetric cones. The Annals of Statistics, 24(2), 763–786.
Chandran, L. S., Ibarra, L., Ruskey, F., Sawada, J. (2003). Generating and characterizing the perfect elimination orderings of a chordal graph. Theoretical Computer Science, 307(2), 303–317.
Dawid, A. P., Lauritzen, S. L. (1993). Hyper Markov laws in the statistical analysis of decomposable graphical models. The Annals of Statistics, 21(3), 1272–1317.
Faraut, J., Korányi, A. (1994). Analysis on symmetric cones. Oxford mathematical monographs. New York: Oxford University Press.
Graczyk, P., Ishi, H. (2014). Riesz measures and Wishart laws associated to quadratic maps. Journal of the Mathematical Society of Japan, 66(1), 317–348.
Graczyk, P., Ishi, H., Kołodziejek, B. (2016). Wishart laws and variance function on homogeneous cones. arXiv:1802.02352
Graczyk, P., Ishi, H., Mamane, S., Ochiai, H. (2017). On the Letac–Massam conjecture on cones \(Q_{A_n}\). Proceedings of the Japan Academy, Series A, Mathematical Sciences, 93(3), 16–21.
Ishi, H. (2014). Homogeneous cones and their applications to statistics. In P. Graczyk, A. Hassairi (Eds.) Modern Methods of Multivariate Statistics, vol. 82, pp. 135–154. Paris: Hermann.
Konno, Y. (2007). Estimation of normal covariance matrices parametrized by irreducible symmetric cones under Stein’s loss. Journal of Multivariate Analysis, 98(2), 295–316.
Konno, Y. (2009). Shrinkage estimators for large covariance matrices in multivariate real and complex normal distributions under an invariant quadratic loss. Journal of Multivariate Analysis, 100(10), 2237–2253.
Kuriki, S., Numata, Y. (2010). Graph presentations for moments of noncentral Wishart distributions and their applications. Annals of the Institute of Statistical Mathematics, 62(4), 645–672.
Lauritzen, S. L. (1996). Graphical models. Oxford statistical series (Vol. 17). New York: Oxford University Press.
Letac, G., Massam, H. (2007). Wishart distributions for decomposable graphs. The Annals of Statistics, 35(3), 1278–1323.
Roverato, A. (2000). Cholesky decomposition of a hyper inverse Wishart matrix. Biometrika, 87(1), 99–112.
Speed, T., Kiiveri, H. (1986). Gaussian Markov distributions over finite graphs. The Annals of Statistics, 14(1), 138–150.
Sugiura, N., Konno, Y. (1988). Entropy loss and risk of improved estimators for the generalized variance and precision. Annals of the Institute of Statistical Mathematics, 40(2), 329–341.
Tsukuma, H., Konno, Y. (2006). On improved estimation of normal precision matrix and discriminant coefficients. Journal of Multivariate Analysis, 97(7), 1477–1500.
Wishart, J. (1928). The generalised product moment distribution in samples from a normal multivariate population. Biometrika, 20A 32–52.
Acknowledgements
The authors would like to thank Gérard Letac and two anonymous referees for their insightful suggestions.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
We list here some properties of triangular matrices, used in proofs.
Lemma 7
-
1.
Let \( A= K^0\), where \(K=A_{\{1:k\}}\) and let L be lower triangular and U upper triangular \(n\times n\) matrices. Then \(UAL=\left( U_{\{1:k\}}KL_{\{1:k\}}\right) ^0. \)
-
2.
Let M, L, U be matrices \(n\times n\), with L lower triangular and U upper triangular. Then, for all \(i=1,\ldots , n\), \((LMU)_{\{1:i\}}= L_{\{1:i\}}M_{\{1:i\}} U_{\{1:i\}}\) and \((UML)_{\{i:n\}}=U_{\{i:n\}}M_{\{i:n\}}L_{\{i:n\}}\).
-
3.
If T is an invertible triangular matrix then \((T_{\{1:k\}})^{-1}=(T^{-1})_{\{1:k\}}\) for all \(k=1,\ldots , n\).
About this article
Cite this article
Graczyk, P., Ishi, H. & Mamane, S. Wishart exponential families on cones related to tridiagonal matrices. Ann Inst Stat Math 71, 439–471 (2019). https://doi.org/10.1007/s10463-018-0647-z
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10463-018-0647-z