Jim Edward Griffin
Names
first: | Jim |
middle: | Edward |
last: | Griffin |
Affiliations
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University College London AND Department of Statistical Science
- website
- location: London
Research profile
author of:
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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.