Bertrand Clarke

Portrait of Bertrand Clarke

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Department Chair, Professor

Areas of expertise:

Data mining and machine learning, prediction,statistical techniques for complex or high-dimensional data, model bias and uncertainty.



Yu, C-W and Clarke, B. (2015) “Median Based Cross-Validation for Model Selection”.Stat. Anal. And Data Mining. Vol. 8, 14-33.(pdf)

Clarke, B. and Clarke, J. (2014) “Estimating proportions in a mixed sample using transcriptomics.”STAT, Vol. 3, 313-325.(pdf)

Clarke, B., Clarke, J. and Yu, C.-W. (2014) Statistical problem classes and their links to information theory. Econ. Reviews, Zellner Memorial Issue, Vol. 33, 337-371(pdf)

Clarke, B. and Clarke, J. (2012) `How to Predict in Several Conventional Settings’.Statistics Surveys, Vol. 6, 1-73(pdf)

Fokoue, E. and Clarke, B. (2011) “Variance Bias Tradeoff for Prequential Model List Selection”.Stat. Papers, Vol. 52, 813-833(pdf)

Clarke, B. and Yuan, A. (2010) “Reference Priors for Empirical Likelihoods.” in: Frontiers of Statistical Decision Making and Bayesian Analysis. Co-Editors: Chen, M., Dey, D., Mueller, P. Sun, D. and Ye, K. Springer, New York, p. 56-68.(pdf)

Yu, C-W and Clarke, B. (2010) “Median Loss Decision Theory”. J. Stat. Planning and Inference, Vol 141, 611-623.(pdf)

Yu, C-W and Clarke, B. (2010) “Asymptotics of Bayesian Median Loss Estimation”J. Mult. Analysis, Vol. 101, No.9, 1950-1958.(pdf)

Clarke, B. (2010) “Desiderata for a Predictive Theory for Statistics”. Bayesian Analysis, Vol. 5, No. 2, 283-318.(pdf)

Clarke, J. and Clarke, B. (2009) “Prequential Analysis of Complex Data with Adaptive Model Reselection”. Stat. Analysis and Data Mining, Vol. 2, No. 4, 274-290.(pdf)

Datta, G., Bhattacharya, A. and Clarke, B. (2008) “Bayesian Tests for the Zero Inflated Poisson Model”. In: Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of P. K. Sen, Balakrishnan, A., Pena, E, and Silvapulle, M. Eds. p. 89-104.(pdf)

Lin, X., Pittman, J. and Clarke, B. (2007). “Information Conversion, Effective Samples & Parametric Size”. Information Theory Transactions. Vol. 53, No. 12, 4438-4456.(pdf)

Clarke, B. (2007). “Information Optimality and Bayes Models”.Journal of Econometrics, Vol. 138, No. 2, 405-429.(pdf)

Clarke, B. and Yuan, A. (2006). “Closed Form Expressions for Bayesian Sample Sizes”. Annals of Statistics, Vol. 34, No. 3, 1293-1330.(pdf)

Clarke, B. and Song, X. (2004). “Approximating the Dependence Structure of Discrete and Continuous Stochastic Processes”. Sankhya A Vol. 66, No. 3, 536-547.(pdf)

Wong, H. and Clarke, B. (2004). “Characterizing Model Weights Given Partial Information in Normal Models”. Statistics and Probability Letters. Vol. 68, No. 1, 27-37.(pdf)

Wong, H. and Clarke, B. (2004). “Improvement over Bayes Prediction in Small Samples in the Presence of Model Uncertainty”.Canadian Journal of Statistics, Vol. 32, No. 3,269-283.(pdf)

Yuan, A. and Clarke, B. (2004). “Asymptotic Normality of the Posterior Given a Statistic”. Canadian Journal of Statistics, Vol. 32, No. 2, 119-137. .(pdf)

Clarke, B. and Yuan, A. (2004). “Partial Information Reference Priors: Derivation and Interpretations”. Journal of Statistical Planning and Inference, Vol. 123, No. 2, 313-345.(pdf)

Gustafson, P. and Clarke, B. (2004). “A Decomposition for the Posterior Variance”.Journal of Statistical Planning and Inference, Vol. 119, No. 2, 311-327.(pdf)

Clarke, B. (2001). “Combining Model Selection Procedures for Online Prediction”. Sankhya, Ser. A, Vol. 63, Part 2, 229-249.(pdf)

Yuan, A. and Clarke, B. (2001). “Manifest Characterization and Testing for Two Latent Traits”.Annals of Statistics., Vol. 29, No. 3, 876-898.(pdf)

Yuan, A. and Clarke, B. (1999). “A Minimally Informative Likelihood for Decision Analysis: Illustration and Robustness”. Canadian Journal of Statistics, Vol. 27, No. 3,649-665.(pdf)

Clarke, B. and Sun, D. (1999). "Asymptotics of the Expected Posterior". Annals of the Institute of Statistical Mathematics, Vol. 51, No. 1, 163-185.(pdf)

Clarke, B. and Gustafson, P. (1998). "On the overall sensitivity of the posterior distribution to its inputs”. Journal of Statistical Planning and Inference, 71: 137-150.(pdf)

Clarke, B. and Sun, D. (1997). "Reference Priors Under the Chi-Square Distance".Sankhya Series A, Vol. 59, Part II, 215-231.(pdf)

Clarke, B. (1996). "Implications of Reference Priors for Prior Information and Sample Size." Journal of the American Statistical Association, 91, 173-184.(pdf)

Clarke, B. and Ghosh, J. K. (1995). "Posterior Convergence Given the Mean."The Annals of Statistics, 23, 2116-2144.(pdf)

Clarke, B. and Barron, A. (1994). "Jeffreys' Prior is Asymptotically Least Favourable Under Entropy Risk." The Journal of Statistical Planning and Inference, 41, 37-60.(pdf)

.Clarke, B. and Wasserman, L. (1993). "Non Informative Priors and Nuisance Parameters."Journal of the American Statistical Association, 88, 1427-1432.(pdf)

Clarke, B. and Barron, A. (1990). "Information Theoretic Asymptotics of Bayes Methods." IEEE Transactions on Information Theory, 36, 453-471.(pdf)

Data Mining and Machine Learning







Research interests:

My main interest these days is in prediction. This is broader than it sounds because prediction brings in questions about model uncertainty (Which model, if any, is true?) model mis-specification (If no model is true, what’s the least bad one?), model complexity (When is more complex modeling better than a simple approach?) and the other sources of variability and bias that have to be small enough for a prediction is useful. Obviously, different model classes can be used to generate predictors but there are also predictors that are not based on any model class. This is the case, for instance, with many machine learning methods such as bagging, boosting, kernel methods, and ensemble methods more generally. In these cases, it is reasonable to ask what the predictor means, i.e., what does a good predictor say about the properties of the phenomenon being predicted? Complex and high dimensional data are the natural places to use predictive techniques since model identification is so hard – even if one believes a model exists (often a dubious assumption). So, I tend to be interested in genomic or other types of complex data where useful formal theory is rare but statistical principles (variance-bias, robustness, complexity minimization, etc.) still provide helpful guidance. Analyzing complex data, or better, developing and understanding good predictors for complex data, often includes clustering, dimension reduction, complexity concepts, ensemble methods – and much else. Indeed, the predictive approach can be regarded as providing an overall conceptualization of the statistical problem in much the same way as Bayes, frequentist, survey sampling, or decision theory does.


Bertrand Clarke earned his PhD in Statistics at the University of Illinois-Champaign-Urbana in 1989.His thesis work was given the Browder J. Thompson award for authors under age 30 of papers in IEEE journals. He spent three years as an Assistant Professor at Purdue University before moving to the University of British Columbia where he worked from 1992-2008. His early research focused on asymptotics, prior selection in Bayesian statistics, and mathematical modeling of biological systems. His first sabbatical was at University College London and his second sabbatical was at Duke University where he was a visiting scholar in the `Large P Small N’ program at SAMSI. In addition, in 2008 he spent three months at the Newton Institute at Cambridge University. He moved to the University of Miami in 2008 and worked for five years at the medical school where he started their MS and PhD programs in biostatistics before coming to Chair the Department of Statistics at the University of Nebraska-Lincoln. His current foci of research are predictive statistics and statistical methodology in genomic data. He has been an associate editor for four different journals, served three years on the Savage Award Committee (best thesis prize in Bayesian statistics), has published numerous papers over several fields, and was made a Fellow of the ASA in 2014. He has also authored one PhD level textbook on data mining and machine learning for Springer, with a complete solutions manual (available to instructors on request).