Thomas RichardsonÕs Bibliography

 

Papers in Refereed Journals

M. Drton, T.S. Richardson (2008). Binary Models for Marginal Independence. Accepted for publication in Journal of the Royal Statistical Society Series B.

 

 

 

S. Chaudhuri, M. Drton, T. S. Richardson (2007). Estimation of a Covariance Matrix with Zeros. Biometrika 94(1), pp. 199-216(18).

 

 

E. Moodie, T.S Richardson, D. Stephens (2007). Demystifying Optimal Dynamic Treatment Regimes.  Accepted for publication in Biometrics.

 

 

 

A. Glynn, J. Wakefield, M. Handcock, T. S. Richardson (2007)  Alleviating Linear Ecological Bias and Optimal Design with Subsample Data.  Accepted for publication in Journal of the Royal Statistical Society Series A.

 

 

 

S. Srinivasan, B. J. Ausk, S. L. Poliachik, S. E. Warner, T. S. Richardson, T. S. Gross (2007) Rest-Inserted Loading Rapidly Amplifies the Response of Bone to Small Increases in Strain and Load Cycles. Accepted for publication in Journal of Applied Physiology

 

 

 

J. A. Wegelin, A. Packer, and T. S. Richardson (2006). Latent models for cross-covariance. Journal of Multivariate Analysis, 97(1): 79-102.

 

 

 

M. Drton and T. S.Richardson (2004). Multimodality of the likelihood in the bivariate seemingly unrelated regression model. Biometrika 91(2): 383-392.

 

 

 

T.S. Richardson (2003). Markov Properties for Acyclic Directed Mixed Graphs. The Scandinavian Journal of Statistics, March 2003, vol. 30, no. 1, pp. 145-157(13).

 

 

 

M. Banerjee and T.S. Richardson (2003). On dualization of graphical Gaussian models; a correction. The Scandinavian Journal of Statistics. March 2003, vol. 30, 817-820.

 

 

 

S. Lauritzen and T.S. Richardson (2002). Chain graph models and their causal interpretations (with discussion). Journal of the Royal Statistical Society Series B. 64(3), 321-363.

 

 

 

T.S. Richardson and P.Spirtes (2002). Ancestral graph Markov models. Annals of Statistics. 30, 962-1030

 

 

 

M. Townsend and T.S. Richardson (2002). Probability and Statistics in the Legal Curriculum: A Case Study in Disciplinary Aspects of Interdisciplinarity. Duquesne Law Review 40(3), pp.447-488.

 

 

 

T. R. Hammond, G. L. Swartzman, T. S. Richardson (2001). Bayesian estimation of fish school cluster composition applied to a Bering Sea acoustic survey. ICES Journal of Marine Science, Vol. 58, No. 6, Nov 2001, pp. 1133-1149

 

 

 

J. Brutlag and T.S. Richardson (1999). A Block Sampling Approach to Distinct Value Estimation. Journal of Computational and Graphical Statistics. 11 ( 2), pp.389 – 404

 

 

 

R.Scheines, C.Glymour, P.Spirtes, C.Meek and T.S. Richardson (1998). The TETRAD Project: Constraint Based Aids to Model Specification. (with discussion) Multivariate Behavioral Research. 33(1) pp.65-118.

 

 

 

P.Spirtes, T.S. Richardson, C.Meek, R. Scheines, C. Glymour (1998). Using Path Diagrams as a Structural Equation Modelling Tool. Sociological Methods and Research, 27 (2), pp.182-225.

 

 

 

T.S. Richardson (1997). A Characterization of Markov Equivalence for Directed Cyclic Graphs. International Journal of Approximate Reasoning, 17, 2/3 (Aug-Oct. 97), pp.107-162,.

 

 

 

G.Cooper, C.Aliferis, R.Ambrosino, J.Aronis, B.Buchanan, R.Caruana, M.Fine, C.Glymour, G.Gordon, B.Hanusa, J.Janosky, C.Meek, T.Mitchell, T.S.Richardson, P.Spirtes (1997).  An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality. Artificial Intelligence and Medicine, 9, pp.107-138.

 

 

Refereed Conference Papers

A. Ali, T. S. Richardson, P. Spirtes, J. Zhang. (2005). Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables. Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence. (F. Bacchus and T. Jaakkola Eds), p.10-17

 

 

 

M. Drton, T.S. Richardson (2004). Iterative Conditional Fitting for Gaussian Ancestral Graph Models. Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, 130-137. (Outstanding student paper award).

 

 

 

M. Drton, T.S. Richardson (2003). A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, pp. 184-191

 

 

 

S. Chaudhuri,  T.S, Richardson (2003). Using the structure of d-connecting paths as a qualitative measure of the strength of dependence. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 116-123.

 

 

 

A. Ali, T.S. Richardson (2002). Markov equivalence classes for maximal  ancestral graphs. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intellgience. pp.1-9.

 

 

 

A. Ali, A. Murua, T.S. Richardson, S. Roy (2001). A Comparison of Traditional Methods and Sequential Bayesian Methods for Blind Deconvolution Problems. 27 pp. Proceedings, EUSIPCO 2002.

 

 

 

J. A. Wegelin, T.S. Richardson  (2001). Cross-covariance modelling via DAGs with hidden variables. Proceedings of the 17th Conference on Uncertainty and Artificial Intelligence.  pp.546-553

 

 

 

T.S. Richardson, H. Bailer and M. Banerjee (1999). Tractable Structure Search in the Presence of Latent Variables. In Proceedings of Artificial Intelligence and Statistics Ô99  (D. Heckerman and J. Whittaker, eds.), Morgan Kaufmann, San Francisco, CA, pp.142-151.

 

 

 

G. Ridgeway, D. Madigan, and T.S. Richardson (1999). Boosting Methodology for Regression Problems. In Proceedings of Artificial Intelligence and Statistics Ô99 (D. Heckerman and J. Whittaker, eds.), Morgan Kaufmann, San Francisco, CA, pp. 152-161.

 

 

 

G.Ridgeway, D.Madigan, T.S. Richardson, and J.O'Kane (1998). Interpretable Boosted Naive Bayes Classification. In Proceedings of the Fourth International Conference on Knowledge  Discovery and Data Mining. (R. Agrawal, P. Stolorz, G. Piatetsky-Shapiro, eds.), pp. 101-104.

 

 

 

T.S. Richardson (1997). Extensions of Undirected and Acyclic, Directed Graphical Models. In Proceedings of Artificial Intelligence and Statistics Õ97, (D. Madigan and P. Smyth, eds.), pp.407-419.

 

 

 

T.S. Richardson, P.Spirtes, C.Glymour (1997). A Note on Cyclic Graphs and Dynamical Feedback Systems. In Proceedings of Artificial Intelligence and Statistics Ô97, (D. Madigan and P. Smyth eds.), pp.421-428.

 

 

 

P.Spirtes, T.S. Richardson (1997). A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias. In Proceedings of Artificial Intelligence and Statistics Ô97, (D. Madigan and P. Smyth eds.), pp.489-500.

 

 

 

P.Spirtes, T.S. Richardson, C.Meek (1997). Heuristic Greedy Search Algorithms for Latent Variable Models. In Proceedings of Artificial Intelligence and Statistics Ô97, (D. Madigan and P. Smyth eds.), pp.481-488.

 

 

 

T.S. Richardson (1996). A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, Portland, Oregon. E.Horvitz and F.Jensen (eds.), Morgan Kaufmann, San Francisco, CA, pp.462- 469.

 

 

 

T.S. Richardson (1996). A Discovery Algorithm for Directed Cyclic Graphs. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, Portland, Oregon, 1996. (E. Horvitz and F. Jensen eds.), Morgan Kaufmann, San Francisco, CA pp.454- 461.

 

 

 

P.Spirtes, C.Meek, and T.S. Richardson (1995). Causal Inference in the Presence of Latent Variables and Selection Bias. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp. 482-487.

 

 

 

T.S. Richardson (1994). Equivalence in Non-Recursive Structural Equation Models. In Proceedings of The 11th Symposium on Computational Statistics, COMPSTAT, 20-26 August 1994, Vienna, Austria. (R.Dutter ed.), Physica Verlag, Vienna, pp.482-487.

 

 

Other Conference Papers

E. Moodie, T.S. Richardson (2005). A new variance for recursive g-estimation of optimal dynamic treatment regimes. Proceedings,  WNAR 2005.

 

 

 

A. Ali, T. Richardson (2004) Searching across Markov equivalence classes of maximal ancestral graphs. Proceedings, JSM 2004.

 

 

 

T.S. Richardson (1999). A Local Markov Property for Acyclic Directed Mixed Graphs. Proceedings, ISI Conference, Helsinki 1999, 4 pp.

 

 

 

T.S. Richardson,         H.Bailer, M. Banerjee (1999). Specification Searches Using MAG Models. Proceedings, ISI Conference, Helsinki 1999, 4 pp.

 

 

Refereed Book Chapters

T.S. Richardson and P.Spirtes (2003). Causal Inference via ancestral graph Markov models (with discussion). In Highly Structured Stochastic Systems, edited by Peter Green, Nils Hjort and Sylvia Richardson to be published by Oxford University Press pp.83-105.

 

 

 

T.S. Richardson (1998). Chain Graphs and Symmetric Associations. In Learning in Graphical Models, (M.Jordan ed.), Kluwer, (republished, 1999, MIT Press), pp.231-259.

 

 

 

S. Andersson, D. Madigan, M. Perlman, and T.S. Richardson (1999). Graphical Markov Models in Multivariate Analysis. In Multivariate Analysis, Design of Experiments, and Survey Sampling, (S. Ghosh ed.), Marcel Dekker.

 

 

Other Book Chapters

T.S. Richardson, L.Schulz, A.Gopnik (2007) Data-mining probabilists or experimental determinists? : A Dialogue on the Principles underlying Causal Learning in Children. In Causal Learning: Psychology, Philosophy and Computation (A.Gopnik, L.Schulz eds.) Oxford: Oxford University Press.

 

 

 

T.S. Richardson, P.Spirtes (1999). Automated discovery of linear feedback models. In Computation, Causation and Discovery, (C.Glymour and G.Cooper eds.), MIT Press, pp.253-302.

 

 

 

R. Scheines, C. Glymour, P. Spirtes, C. Meek and T.S. Richardson (1999). Truth is among the best explanations: Finding causal explanations of conditional independence and dependence. In Computation, Causation and Discovery, (C. Glymour and G. Cooper eds.), MIT Press, pp.167-209.

 

 

 

P.Spirtes, C. Meek, T.S. Richardson (1999). An algorithm for causal inference in the presence of latent variables and selection bias. In Computation, Causation and Discovery (C.Glymour and G.Cooper eds.), MIT Press, pp.211-252.

 

 

Discussions

J. Robins, T.J. vanderWeele, T.S. Richardson (2007). Comment on Causal effects in the presence of non compliance a latent variable interpretation by A. Forcina. Metron  LXIV (3) pp.288-298.

 

 
 

T.S. Richardson (2004) Contribution to discussion of paper on Ecological Inference by J. Wakefield. Journal of the Royal Statistical Society, 167(3) Ser A.
 

 
 

C. Glymour, P. Spirtes and T.S. Richardson (1999). On the possibility of inferring causation from association without background knowledge. A response to a paper by J. Robins and L. Wasserman, and reply to a rejoinder. In Computation, Causation and Discovery, (C.Glymour and G.Cooper eds.), MIT Press, pp.323-332, pp.343-345.

 

 

 

T.S. Richardson (1999). Discussion of Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models, by B. Thiesson,   M. Chickering, D. Heckerman, and C. Meek. Bayesian Statistics 6, to appear 1999.

 

 

 

G. Ridgeway, T.S. Richardson, and D. Madigan (1999). Discussion of Bump Hunting in High-Dimensional Data by J. Friedman and N. Fisher. Statistics and Computing, 9(2), pp.150-152.

 

 

Book Review

T.S. Richardson (1997). Review of An Introduction to Bayesian Networks by F.V. Jensen. Journal of the American Statistical Association, 92 (439) pp.1215-1216.

 

 

Books Edited

T. Jaakkola, T.S. Richardson (2001) Proceedings of the Eighth International Conference on Artificial Intelligence and Statistics. Morgan Kaufmann.

 

 

 

R. Dechter, T.S. Richardson (2006) Proceedings of the Twenty-Second Conference on Uncertainty and Artificial Intelligence. AUAI Press.

 

 
Editorial

T.S. Richardson (2000). Prediction and Model Selection. Statistics and Computing.

 

 

Technical Reports &

Submitted Papers

S. Chaudhuri, T.S. Richardson , J. Robins, and E. Zivot (2007). Split-Sample Score Tests in Linear Instrumental Variables Regression. CSSS Working paper no.73. Submitted to Econometric Theory.

 

E. Moodie, T.S. Richardson (2007). Bias Correction in Non-Differentiable Estimating Equations for Optimal Dynamic Regimes. COBRA Preprint Series. Article 17. Submitted to Scandinavian Journal of Statistics.

 

 

 

M. Miyamura, T.S. Richardson (2006). Bi-partial covariances and Gaussian ancestral graph models. Submitted to Probability Theory and Related Fields.

 

 

 

M. Drton, M. Eichler, T.S. Richardson (2006). Identification and likelihood inference for recursive linear models with correlated errors. arXiv:math.ST/0601631. Submitted to JASA.

 

 

 

T.S. Richardson (2006) A factorization criterion for ancestral graphs. Work in progress.

 

 

 

M. Drton, T.S. Richardson (2004). Graphical Answers to Questions About Likelihood Inference in Gaussian Covariance Models. Department of Statistics, University of Washington, Tech. Report 467.

 

 

 

D. Heckerman, C. Meek, T.S. Richardson (2004) Variations on undirected graphical models and their relationships. Unpublished Technical Report.

 

 

 

A. Ali, T.S. Richardson and P. Spirtes (2004) Markov Equivalence for Ancestral Graphs. Department of Statistics, University of Washington, Tech. Report 464.

 

 

 

J. A. Wegelin, T.S. Richardson and D. L. Ragozin (2001). Rank-One Latent Models for Cross-Covariance. UW Department of Statistics, Technical Report, No. 391, 29 pp.

 

 

 

T.S. Richardson  (2001) Chain graphs which are maximal ancestral graphs are recursive causal graphs.  UW Department of Statistics, Technical Report, No. 387, 13 pp.

 

 

 

N. Wermuth, D.R. Cox, T.S. Richardson and G. Glonek (1999). On transforming and generating cyclic graph models. ZUMA Technical Report. 12 pp.

 

 

 

T.S. Richardson (1996). Fast re-calculation of the covariance matrix implied by a recursive structural equation model. Technical Report, CMU-PHIL-67, 9 pp.

 

 

 

P.Spirtes, T.S. Richardson, C.Meek, R.Scheines and C.Glymour (1996). Using d-separation to calculate zero partial correlations in linear models with correlated errors., Technical Report, CMU-PHIL-72. 17pp.