Professor Jim Griffin’s research page
I am Professor of Statistics at the School of Mathematics, Statistics and Actuarial Science, University of Kent. My contact details are here and my interests are:
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.
An adaptive truncation method for inference in Bayesian nonparametric models
Hierarchical sparsity priors for regression models (with P. J. Brown)
Adaptive MC3 and Gibbs Algorithms for Bayesian Model Averaging in Linear Regression Models (with D. S. Lamnisos and M. F. J. Steel)
Two-sample Bayesian nonparametric hypothesis testing (with C. C. Holmes, F. Caron and D. A. Stephens)
Flexible Modelling of Dependence in Volatility Processes (with M. Kalli), Journal of Business and Economic Statistics.
Time-Varying Sparsity in Dynamic Regression Models (with M. Kalli), Journal of Econometrics, 178, 779-793. (Matlab code is available for this paper)
Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models (with D. S. Lamnisos and M. F. J. Steel), Journal of Computational and Graphical Statistics, 22, 729-748.
Some Priors for Sparse Regression Modelling (with P. J. Brown), Bayesian Analysis, 8, 691-702.
Comparing Distributions By Using Dependent Normalized Random-Measure Mixtures (with M. Kolossiatis and M. F. J. Steel), Journal of the Royal Statistical Society, Series B, 75, 499-529.
A Bayesian Semiparametric Model for Volatility Modelling with a Leverage Effect (with E.-I. Delatola), Computational Statistics and Data Analysis, 60, 97-110.
On Bayesian nonparametric modelling of two correlated distributions (with M. Kolossiatis and M. F. J. Steel), Statistics and Computing, 23, 1-15.
On Adaptive Metropolis-Hastings Methods (with S. G. Walker), Statistics and Computing, 23, 123-134.
Bayesian correlated clustering to integrate multiple datasets (with P. W. D. Kirk, R. S. Savage, Z. Ghahramani and D. L. Wild), Bioinformatics, 28, 3290-3297.
Structuring shrinkage: some correlated priors for regression (with P. J. Brown), Biometrika, 99, 481-487.
Cross-validation prior choice in Bayesian probit regression with many covariates (with D. Lamnisos and M. F. J. Steel), Statistics and Computing, 22, 359-373.
Bayesian adaptive lassos with non-convex penalization (with P. J. Brown), Australian and New Zealand Journal of Statistics, 53, 423-442.
Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility (with E.-I. Delatola), Bayesian Analysis, 6, 901-926. (Matlab code is available)
Bayesian Clustering of Distributions in Stochastic Frontier Analysis, Journal of Productivity Analysis, 36, 275-283.
Inference in Infinite Superpositions of non-Gaussian Ornstein-Uhlenbeck processes using Bayesian nonparametric methods, Journal of Financial Econometrics, 9, 519-549.
The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference, Journal of Statistical Planning and Inference, 141, 3648-3664.
Modelling overdispersion with the Normalized Tempered Stable distribution (with M. Kolossiatis and M. F. J. Steel), Computational Statistics and Data Analysis, 55, 2288-2301.
Stick-Breaking Autoregressive Processes (with M. F. J. Steel), Journal of Econometrics, 162, 383-396.
Posterior Simulation of Normalized Random Measure Mixtures (with S. G. Walker), Journal of Computational and Graphical Statistics, 20, 241-259. (Matlab code is available)
Slice Sampling Mixture Models (with M. Kalli and S. G. Walker), Statistics and Computing, 21, 93-105.
(with R. C. A. Oomen), Journal of Econometrics, 160, 58-68.
Bayesian Nonparametric Modelling with the Dirichlet Process Regression Smoother (with M. F. J. Steel), Statistica Sinica, 20, 1507-1527.
Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes (with M. F. J. Steel), Computational Statistics and Data Analysis, 54, 2594-2608. (Matlab code is available)
Discovering Transcriptional Modules from Bayesian Data Fusion (with R. S. Savage, Z. Ghahramani, B. J. de la Cruz and D. L. Wild), Bioinformatics, 26, 1158-1167.
Inference with Normal-Gamma prior distributions in regression problems (with P. J. Brown), Bayesian Analysis, 5, 171-188.
Default priors for density estimation with mixture models (Matlab code is available), Bayesian Analysis, 5, 45-64.
Transdimensional sampling algorithms for Bayesian variable selection in classification problems with many more variables than observations (with D. Lamnisos and M. F. J. Steel), Journal of Computational and Graphical Statistics, 18, 592-612. (Matlab Code and a ReadMe file are available)
Flexible Mixture Modelling of Stochastic Frontiers (with M. F. J. Steel), Journal of Productivity Analysis, 29, 33-50.
Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time? (with R. C. A. Oomen), Econometric Reviews, 27, 230-253.
Bayesian Stochastic Frontier Analysis Using WinBUGS (with M. F. J. Steel), Journal of Productivity Analysis, 27, 163-176. (WinBugs code is available)
Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility (with M. F .J. Steel), Journal of Econometrics, 134, 605-644.
Order-Based Dependent Dirichlet Processes (with M. F. J. Steel), Journal of the American Statistical Association, Theory and Methods, 101, 179-194.
Semiparametric Bayesian Inference for Stochastic Frontier Models (with M. F. J. Steel), Journal of Econometrics, 123, 121-152. (Matlab code is available)
(with C. J. Hoggart), in Bayesian Methods with Applications to Science, Policy and Official Statistics (Selected Papers from ISBA 2000): The Sixth World Meeting of the International Society for Bayesian Analysis, 223-232.
Andrea Cremaschi (
Sam Oduro (
Su Wang (
Bill Sakaria (
Vasiliki Dimitrakopoulou (University of Kent) – “Bayesian Variable Selection in Cluster Analysis” (with Phil Brown)
Eleni-Ioanna Delatola (University of Kent) – “Bayesian Nonparametric Modelling of Financial Data”
Kitty Wan (University of Kent) – “Statistical Methods for the Analysis of Genetic Association Studies” (with Phil Brown)
Michalis Kolossiatis (University of Warwick) – “Modelling via Normalisation for Parametric and Nonparametric Inference” (with Mark Steel)
Demetris Lamnisos (University of Warwick) – “Bayesian Variable Selection for Binary Regression with Many More Variables than Observations” (with Mark Steel)