Jim Edward Griffin

Names

Affiliations

• University College London AND Department of Statistical Science
  • website
  • location: London

Research profile

author of:

• Bayesian stochastic frontier analysis using WinBUGS
  by Jim Griffin & Mark Steel
• Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility
  by Griffin, J. E. & Steel, M. F. J.
• Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes
  by Griffin, Jim & Steel, Mark F. J.
• Order-Based Dependent Dirichlet Processes
  by Griffin, J. E. & Steel, M. F. J.
• Semiparametric Bayesian inference for stochastic frontier models
  by Griffin, J. E. & Steel, M. F. J.
• Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time?
  by Jim Griffin & Roel Oomen
• Semiparametric Bayesian Inference for Stochastic Frontier Models
  by Jim E. Griffin & Mark F. J. Steel
• Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes
  by Griffin, J. E. & Steel, M. F. J.
• Flexible mixture modelling of stochastic frontiers
  by J. Griffin & M. Steel
• Bayesian Stochastic Frontier Analysis Using WinBUGS
  by Jim Griffin & Mark Steel
• Modeling overdispersion with the normalized tempered stable distribution
  by Kolossiatis, M. & Griffin, J. E. & Steel, M. F. J.
• Covariance measurement in the presence of non-synchronous trading and market microstructure noise
  by Griffin, Jim E. & Oomen, Roel C. A.
• Stick-breaking autoregressive processes
  by Griffin, J. E. & Steel, M. F. J.
• Inference in Infinite Superpositions of Non-Gaussian Ornstein--Uhlenbeck Processes Using Bayesian Nonparametic Methods
  by J. E. Griffin
• Bayesian clustering of distributions in stochastic frontier analysis
  by J. Griffin
• Structuring shrinkage: some correlated priors for regression
  by J. E. Griffin & P. J. Brown
• A Bayesian semiparametric model for volatility with a leverage effect
  by Delatola, E.-I. & Griffin, J. E.
• Time-varying sparsity in dynamic regression models
  by Kalli, Maria & Griffin, Jim E.
• Comparing distributions by using dependent normalized random-measure mixtures
  by J. E. Griffin & M. Kolossiatis & M. F. J. Steel
• On efficient Bayesian inference for models with stochastic volatility
  by Sakaria, D. K. & Griffin, J. E.
• Flexible Modeling of Dependence in Volatility Processes
  by Maria Kalli & Jim Griffin
• Bayesian Nonparametric Estimation of Ex-post Variance
  by Griffin, Jim & Liu, Jia & Maheu, John M.
• Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics
  by Jim Griffin & Maria Kalli & Mark Steel
• Compound random measures and their use in Bayesian non-parametrics
  by Jim E. Griffin & Fabrizio Leisen
• Bayesian nonparametric vector autoregressive models
  by Kalli, Maria & Griffin, Jim E.