David Nott

Associate Professor
Department of Statistics and Applied Probability
National University of Singapore

Contact Information

E-mail: standj (at) nus.edu.sg

Mailing Address:
Dept. of Statistics and Applied Probability,
Blk S16, Level 7,
Faculty of Science,
6 Science Drive 2,
National University of Singapore,
Singapore 117546

Office: 07-109, Block S16

Research Interests
Bayesian model selection, Bayesian nonparametrics, hierarchical models, Markov chain Monte Carlo, spatio-temporal modelling.

Selected recent publications
Cottet, Remy, Kohn, Robert and Nott, David J. (2008). Variable selection and model averaging in semiparametric overdispersed generalized linear models. Journal of the American Statistical Association, 103, 667-671.

Nott, David J. (2008). Predictive performance of Dirichlet process shrinkage methods in linear regression. Computational Statistics and Data Analysis, 52(7), 3658-3669.

Leslie, David, Kohn, Robert and Nott, David J. (2007). A general approach to heteroscedastic linear regression. Statistics and Computing, 17(2), 131-146.

Nott, David J. and Kuk, Anthony Y.C. (2007). Coefficient sign prediction methods for model selection. Journal of the Royal Statistical Society, Series B, 69, 447-461.

Marshall, L., Sharma, A. and Nott, David J. (2007). A single model ensemble versus a dynamic modelling platform: Semi-distributed rainfall runoff modeling in a hierarchical mixtures of experts framework. Geophysical Research Letters, 34.

Marshall, L., Nott, David J. and Sharma, A. (2007). Towards dynamic catchment modelling: A Bayesian hierarchical mixtures of experts framework. Hydrological Processes, 21, 847-861.

Nott, David J. (2006). Semiparametric estimation of mean and variance functions for non-Gaussian data. Computational Statistics, 21, 603-620.

Chan, David, Kohn, Robert, Nott, David J. and Kirby, Chris (2006). Adaptive nonparametric estimation of mean and variance functions. Journal of Computational and Graphical Statistics, 15, 915-936.

Leonte, Daniela and Nott, David J. (2006). Spatial modelling of gamma ray count data. Mathematical Geology, 38, 135-154.

Nott, David J., Yu, Zeming, Chan, Eva, Cotsapas, Chris, Cowley, Mark, Pulvers, Jeremy, Williams, Rohan and Little, Peter (2006). Hierarchical Bayes variable selection and microarray experiments. Journal of Multivariate Analysis, 98, 852-872.

Cotsapas, Chris J., Williams, Rohan B., Pulvers, Jeremy N., Nott, David J., Chan Eva K.F., Cowley, Mark J. and Little, Peter F.R. (2006). Genetic dissection of gene regulation in multiple mous tissues. Mammalian Genome, 17, 490-495.

Cripps, Edward, Kohn, Robert and Nott, David J. (2006). Bayesian subset selection and model averaging using centred and dispersed priors. Australian and New Zealand Journal of Statistics, 48(2), pp. 237--252.

Williams, Rohan, Cotsapas, Chris, Cowley, Mark, Chan, Eva, Nott, David J. and Little, Peter F.R. (2006). Influence of microarray normalisation procedures on detection of linkage signal in genetical-genomics experiments. Nature Genetics, 38(8), 855-56.

Dahlke, Isabelle, Nott, David J., Ruhno, John, Sewell, William A. and Collins, Andrew M. (2006). Antigen selection in the IgE response of allergic and non-allergic individuals. Journal of Allergy and Clinical Immunology, 117(6), 1477-1483.

Nott, David J. and Kohn, Robert (2005). Adaptive sampling for Bayesian variable selection. Biometrika, 92, 747-763.

Nott, David J., Kuk, A.Y.C. and Duc, H. (2005). Efficient sampling schemes for Bayesian MARS models with many predictors. Statistics and Computing, 15, 93--101.

Cripps, Edward, Nott, David J., Dunsmuir, William T.M. and Wikle, C. (2005). Space-time modelling of Sydney Harbour winds. Australian and New Zealand Journal of Statistics, 47, 3--18.

Nott, David J. and Leonte, Daniela (2004). Sampling schemes for Bayesian variable selection in generalized linear models. Journal of Computational and Graphical Statistics, 13, 362--382.

Nott, David J. and Green, Peter J. (2004). Bayesian variable selection and the Swendsen-Wang algorithm. Journal of Computational and Graphical Statistics, 13, 141--157.