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Jauch, Michael; Hoff, Peter D.; Dunson, David B.

Random orthogonal matrices and the Cayley transform. (English) Zbl 1466.60010

Bernoulli 26, No. 2, 1560-1586 (2020).
Summary: Random orthogonal matrices play an important role in probability and statistics, arising in multivariate analysis, directional statistics, and models of physical systems, among other areas. Calculations involving random orthogonal matrices are complicated by their constrained support. Accordingly, we parametrize the Stiefel and Grassmann manifolds, represented as subsets of orthogonal matrices, in terms of Euclidean parameters using the Cayley transform. We derive the necessary Jacobian terms for change of variables formulas. Given a density defined on the Stiefel or Grassmann manifold, these allow us to specify the corresponding density for the Euclidean parameters, and vice versa. As an application, we present a Markov chain Monte Carlo approach to simulating from distributions on the Stiefel and Grassmann manifolds. Finally, we establish that the Euclidean parameters corresponding to a uniform orthogonal matrix can be approximated asymptotically by independent normals. This result contributes to the growing literature on normal approximations to the entries of random orthogonal matrices or transformations thereof.

Cited in 4 Documents

MSC:

60B20 Random matrices (probabilistic aspects)
60F05 Central limit and other weak theorems
62R30 Statistics on manifolds

Keywords:

Gaussian approximation; Grassmann manifold; Jacobian; Markov chain Monte Carlo; Stiefel manifold

Software:

Stan; PyMC; rstiefel
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\textit{M. Jauch} et al., Bernoulli 26, No. 2, 1560--1586 (2020; Zbl 1466.60010)
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