@article { ISI:000276284600019,
	title = {Generating random AR(p) and MA(q) Toeplitz correlation matrices},
	journal = {Journal of Multivariate Analysis},
	volume = {101},
	number = {6},
	year = {2010},
	month = {JUL},
	pages = {1532-1545},
	publisher = {Elsevier Inc},
	type = {Article},
	abstract = {Methods are proposed for generating random (p+1) x (p+1) Toeplitz correlation matrices that are consistent with a causal AR(p) Gaussian time series model. The main idea is to first specify distributions for the partial autocorrelations that are algebraically independent and take values in (-1, 1), and then map to the Toeplitz matrix. Similarly, starting with pseudopartial autocorrelations, methods are proposed for generating (q+1) x (q+1) Toeplitz correlation matrices that are consistent with an invertible MA(q) Gaussian time series model. The density can be uniform or non-uniform over the space of autocorrelations up to lag p or q, or over the space of autoregressive or moving average coefficients, by making appropriate choices for the densities of the (pseudo)-partial autocorrelations. Important intermediate steps are the derivations of the Jacobians of the mappings between the (pseudo)-partial autocorrelations, autocorrelations and autoregressive/moving average coefficients. The random generating methods are useful for models with a structured Toeplitz matrix as a parameter. (C) 2010 Elsevier Inc. All rights reserved.},
	keywords = {Autoregressive process, Beta distribution, Longitudinal data, Moving average process},
	issn = {0047-259X},
	doi = {10.1016/j.jmva.2010.01.013},
	author = {Ng, C. T. and Joe, Harry}
}
@article { ISI:000241970400006,
	title = {Generating random correlation matrices based on partial correlations},
	journal = {Journal of Multivariate Analysis},
	volume = {97},
	number = {10},
	year = {2006},
	month = {NOV},
	pages = {2177-2189},
	publisher = {Elsevier Inc},
	type = {article},
	abstract = {A d-dimensional positive definite correlation matrix R = (rho(ij)) can be parametrized in terms of the correlations rho(i,i+1) for i = 1,..., d - 1, and the partial correlations rho(ij\textbackslashi+1,.... j-1) for j - i >= 2. These ((d)(2)) parameters can independently take values in the interval (- 1, 1). Hence we can generate a random positive definite correlation matrix by choosing independent distributions F-ij, 1 <= i < j <= d, for these ((d)(2)) parameters. We obtain conditions on the F-ij so that the joint density of (rho(ij)) is proportional to a power of det(R) and hence independent of the order of indices defining the sequence of partial correlations. As a special case, we have a simple construction for generating R that is uniform over the space of positive definite correlation matrices. As a byproduct, we determine the volume of the set of correlation matrices in ((d)(2))-dimensional space. To prove our results, we obtain a simple remarkable identity which expresses det(R) as a function of rho(i,i+1) for i = 1,..., d - 1, and p(ij\textbackslashi+1,... j-1) for j - i >= 2. (C) 2005 Elsevier Inc. All rights reserved.},
	keywords = {Beta distribution, determinant of correlation matrix},
	issn = {0047-259X},
	doi = {10.1016/j.jmva.2005.05.010},
	author = {Joe, Harry}
}
