Statistics

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Professor Jim Griffin

Professor of Statistics, Director of Research

SMSAS - Statistics Group

Room: E116
Modules taught:
MA632: Regression
MA882: Advanced Regression Modelling

Office hours

Research Interests:

  • Bayesian nonparametric methods including slice sampling for posterior simulation and the construction of dependent nonparametric priors.
  • Inference with financial data including the analysis of high frequency data, volatility processes with jumps and the application of Bayesian nonparametric methods to stochastic volatility modelling.
  • Bayesian methods for variable selection methods with many regressors including efficient computation, the use of shrinkage priors and applications in bioinformatics.
  • Efficiency measurement using stochastic frontier models.

Jim serves on the School's Graduate Studies Committee and he is the Director of Studies and Admissions Officer for MSc programmes in Statistics/Statistics with Finance. He is the School's Director of Research and chairs our Research and Enterprise Committee.

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Also view these in the Kent Academic Repository

Article
Griffin, J.E. and Kalli, M. (2015). Flexible Modelling of Dependence in Volatility Processes. Journal of Business and Economic Statistics [Online] 33:102-113.
Kalli, M. and Griffin, J.E. (2014). Time-varying sparsity in dynamic regression models. Journal of Econometrics [Online] 178:779-793.
Griffin, J.E. and Walker, S.G. (2013). On adaptive Metropolis-Hastings method. Statistics and Computing [Online] 23:123-134.
Griffin, J.E., Kolossiatis, M. and Steel, M.F.J. (2013). Comparing distributions by using dependent normalized random-measure mixtures . Journal of the Royal Statistical Society: Series B (Statistical Methodology) [Online] 75:499-529.
Griffin, J.E. and Delatola, E.-I. (2013). A Bayesian semiparametric model for volatility with a leverage effect. Computational Statistics and Data Analysis [Online] 60:97-110.
Kolossiatis, M., Griffin, J.E. and Steel, M.F.J. (2013). On Bayesian nonparametric modelling of two correlated distributions. Statistics and Computing [Online] 23:1-15.
Griffin, J.E. and Brown, P.J. (2013). Some Priors for Sparse Regression Modelling. Bayesian Analysis [Online] 8:691-702.
Lamnisos, D., Griffin, J.E. and Steel, M.F.J. (2013). Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models. Journal of Computational and Graphical Statistics [Online] 22:729-748.
Lamnisos, D., Griffin, J.E. and Steel, M.F.J. (2012). Cross-validation prior choice in Bayesian probit regression with many covariates. Statistics and Computing [Online] 22:359-373.
Griffin, J.E. and Brown, P.J. (2012). Structuring Shrinkage: Some Correlated Priors for Regression. Biometrika [Online] 99:481-487.
Kirk, P. et al. (2012). Bayesian correlated clustering of integrated multiple datasets. Bioinformatics [Online] 28:3290-3297.
Griffin, J.E. (2011). Bayesian clustering of distributions in stochastic frontier analysis. Journal of Productivity Analysis [Online] 36:275-283.
Kolossiatis, M., Griffin, J.E. and Steel, M.F.J. (2011). Modeling overdispersion with the Normalized Tempered Stable distribution. Computational Statistics and Data Analysis [Online] 55:2288-2301.
Griffin, J.E. and Steel, M.F.J. (2011). Stick-Breaking Autoregressive Processes. Journal of Econometrics [Online] 162:383-396.
Delatola, E.-I. and Griffin, J.E. (2011). Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility. Bayesian Analysis [Online] 6:901-926.
Griffin, J.E. (2011). Inference in Infinite Superpositions of Non-Gaussian Ornstein–Uhlenbeck Processes Using Bayesian Nonparametic Methods. Journal of Financial Econometrics [Online] 9:519-549.
Kalli, M., Griffin, J.E. and Walker, S.G. (2011). Slice Sampling Mixture Models. Statistics and Computing [Online] 21:93-105.
Griffin, J.E. (2011). The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference. Journal of Statistical Planning and Inference [Online] 141:3648-3664.
Griffin, J.E. and Walker, S.G. (2011). Posterior Simulation of Normalized Random Measure Mixtures. Journal of Computational and Graphical Statistics 20:241-259.
Griffin, J.E. and Brown, P.J. (2011). Bayesian hyper-lassos with non-convex penalization. Australian and New Zealand Journal of Statistics [Online] 53:423-442.
Griffin, J.E. and Oomen, R.C.A. (2011). Covariance measurement in the presence of non-synchronous trading and market microstructure noise. Journal of Econometrics [Online] 160:58-68.
Savage, R.S. et al. (2010). Discovering Transcriptional Modules from Bayesian Data Fusion. Bioinformatics [Online] 26:1158-1167.
Griffin, J.E. and Steel, M.F.J. (2010). Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes. Computational Statistics and Data Analysis [Online] Online:2594-2608.
Griffin, J.E. (2010). Default priors for density estimation with mixture models. Bayesian Analysis [Online] 5:45-64.
Griffin, J.E. and Steel, M.F.J. (2010). Bayesian Nonparametric Modelling with the Dirichlet Process Regression Smoother. Statistica Sinica [Online] 20:1507-1527.
Griffin, J.E. and Brown, P.J. (2010). Inference with normal-gamma prior distributions in regression problems. Bayesian Analysis [Online] 5:171-188.
Lamnisos, D., Griffin, J.E. and Steel, M.F.J. (2009). Transdimensional Sampling Algorithms for Bayesian Variable Selection in Classification Problems With Many More Variables Than Observations. Journal of Computational and Graphical Statistics [Online] 18:592-612.
Griffin, J.E. and Steel, M.F.J. (2008). Flexible mixture modelling of stochastic frontiers. Journal of Productivity Analysis [Online] 29:33-50.
Griffin, J.E. and Oomen, R.C.A. (2008). Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time?. Econometric Reviews [Online] 27:230-253.
Griffin, J.E. and Steel, M.F.J. (2007). Bayesian Stochastic Frontier Analysis Using WinBUGS. Journal of Productivity Analysis [Online] 27:163-176.
Griffin, J.E. and Steel, M.F.J. (2006). Inference with non-Gaussian Ornstein–Uhlenbeck processes for stochastic volatility. Journal of Econometrics [Online] 134:605-644.
Griffin, J.E. and Steel, M.F.J. (2006). Order-Based Dependent Dirichlet Processes. Journal of the American Statistical Association [Online] 101:179-94.
Griffin, J.E. and Steel, M.F.J. (2004). Semiparametric Bayesian inference for stochastic frontier models. Journal of Econometrics [Online] 123:121-152.
Book section
Griffin, J.E., Quintana, F. and Steel, M.F.J. (2011). Flexible and Nonparametric Methods. In: The Oxford Handbook Of Bayesian Econometrics. Oxford University Press.
Griffin, J.E. and Holmes, C.C. (2010). Computational issues arising in Bayesian nonparametric hierarchical models. In: Hjort, N. L. et al. eds. Bayesian Nonparametrics. Cambridge University Press.
Research report (external)
Kalli, M., Griffin, J.E. and Walker, S.G. (2008). Slice Sampling Mixture Models. Centre for Health Services Studies.
Conference or workshop item
Hoggart, C.J. and Griffin, J.E. (2001). A Bayesian Partition Model for Customer Attrition. In: Isba 2000: The Sixth World Meeting Of The International Society For Bayesian Analysis. pp. 223-232.
Monograph
Hodges, S.D. et al. (2001). Non-Gaussian Ornstein-Uhlenbeck-Based Models And Some Of Their Uses In Financial Economics - Discussion. BLACKWELL PUBLISHING LTD.
Total publications in KAR: 38 [See all in KAR]
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Last Updated: 20/11/2014