Currently browsing: Items authored or edited by Catriona Queen

Costa, Lilia; Anacleto, Osvaldo; Nascimento, Diego C.; Smith, James Q.; Queen, Catriona; Louzada, Francisco and Nichols, Thomas (2024). Evaluating brain group structure methods using hierarchical dynamic models. Pattern Recognition, 155, article no. 110687.

• Request a copy from the author This document will be available to download from 1 July 2025

Anacleto, Osvaldo and Queen, Catriona (2017). Dynamic chain graph models for time series network data. Bayesian Analysis, 12(2) pp. 491–509.

• Download Published Version (PDF / 374kB)

Anacleto Junior, Osvaldo; Queen, Catriona and Albers, Casper (2013). Forecasting multivariate road traffic flows using Bayesian dynamic graphical models, splines and other traffic variables. Australian & New Zealand Journal of Statistics, 55(2) pp. 69–86.

• Download Accepted Manuscript (PDF / 625kB)

Anacleto Junior, Osvaldo; Queen, Catriona and Albers, Casper (2013). Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors. Journal of the Royal Statistical Society: Series C (Applied Statistics), 62(2) pp. 251–270.

• Download Accepted Manuscript (PDF / 573kB)

Queen, Catriona and Albers, Casper (2009). Intervention and causality: forecasting traffic flows using a dynamic Bayesian network. Journal of the American Statistical Association, 104(486) pp. 669–681.

• Download Accepted Manuscript (PDF / 493kB)

Queen, Catriona M.; Wright, Ben J. and Albers, Casper J. (2008). Forecast covariances in the linear multiregression dynamic model. Journal of Forecasting, 27(2) pp. 175–191.

• Download Accepted Manuscript (PDF / 462kB)

Queen, Catriona M.; Wright, Ben J. and Albers, Casper J. (2007). Eliciting a directed acyclic graph for a multivariate time series of vehicle counts in a traffic network. Australian & New Zealand Journal of Statistics, 49(3) pp. 221–239.

• Download Accepted Manuscript (PDF / 1MB)

Whitlock, Mark E. and Queen, Catriona M. (2000). Modelling a traffic network with missing data. Journal of Forecasting, 19(7) pp. 561–574.