Associate Professor
Department of Statistics and Data Science
National University of Singapore
Contact Information
E-mail: standj@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
Tan, S.L.L. and Nott, David J. (2013). Variational Inference for Generalized Linear Mixed Models Using Partially Noncentered Parametrizations. Statistical Science, 28, 168-188.
Nott, David J., Marshall, L. and Brown, J. (2012). Generalized likelihood uncertainty estimation (GLUE) and approximate Bayesian computation: What’s the connection? Water Resources Research, 48.
Villani, M., Kohn, R. and Nott, David J. (2012). Generalized Finite Smooth Mixtures. Journal of Econometrics, 171, 121-133.
Marshall, L., Tran, M.-N and Nott, David J. (2012). The ensemble Kalman filter is an ABC algorithm. Statistics and Computing, 22, 1273-1276.
Tran, M.-N, Nott, David J. and Leng, C. (2012). The Predictive Lasso. Statistics and Computing, 22, 1069-1084.
Nott, David J., Tan, S.L.L., Villani, M. and Kohn, R. (2012). Regression density estimation with variational methods and stochastic approximation. Journal of Computational and Graphical Statistics, 21, 797-820.
Tran, M.-N, Nott, David J. and Kohn, R. (2012). Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts. Electronic Journal of Statistics, 6, 1170-1199.
Nott, David J., Tran, M.-N., and Leng, C. (2012). Variational approximation for heteroscedastic linear models and matching pursuit algorithms. Statistics and Computing, 22, 497-512.
Nott, David J., Fielding M.J. and Li, J. (2011). Importance sampling as a variational approximation. Statistics and Probability Letters, 81, 1052-1055.
Fielding, M.J., Nott, David J. and Liong, S.-Y. (2011). Efficient MCMC schemes for computationally expensive posterior distributions. Technometrics, 53, 16-28.
Nott, David J. and Leng, C. (2010). Bayesian projection approaches to variable selection in generalized linear models. Computational Statistics & Data Analysis, 54, 3227-3241.
Nott, David J. and Li, J. (2010). A sign based loss approach to model selection in nonparametric regression. Statistics and Computing, 20, 485-498.
Nott, David J., Fielding, M.J. and Leonte, D. (2009). On a generalization of the Laplace approximation. Statistics and Probability Letters, 79, 1397-1403.
Nott, David J. and Kuk, A.Y.C. (2009). Analysis of clustered binary data with unequal cluster sizes: A semiparametric Bayesian approach. Journal of Agricultural, Biological, and Environmental Statistics, 15, 101-118.
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.