引用次数
引用: 7,105
H-指数: 19
I10-指数 : 21
出版物: 50
Optimal scaling of MCMC for conditioned diffusions
D. White , M. Pitt , A. M. Stuart
2008
Constructing First Order Stationary Autoregressive Models via Latent Processes
MICHAEL K. PITT , CHRIS CHATFIELD , STEPHEN G. WALKER
Scandinavian Journal of Statistics 29 ( 4) 657 -663
52
2002
Likelihood based inference for diffusion driven models.
Neil Shephard , Siddhartha Chib , Michael K Pitt
Research Papers in Economics
47
2004
Likelihood based inference for diffusion driven models
Neil Shephard , Siddhartha Chib , Michael K Pitt
Economics Papers
2004
Constructing Stationary Time Series Models Using Auxiliary Variables With Applications
Michael K Pitt , Stephen G Walker
Journal of the American Statistical Association 100 ( 470) 554 -564
35
2005
Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics-Discussion
SD Hodges , G Roberts , O Papaspiliopoulos , E Sentana
Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63 208 -241
1
2001
Large sample asymptotics of the pseudo-marginal method
Sebastian M Schmon , George Deligiannidis , Arnaud Doucet , Michael K Pitt
Biometrika 108 ( 1) 37 -51
38
2020
Particle filters for state and parameter estimation of cox process
Omiros Papaspiliopoulos , Miguel AG Belmonte , Michael K Pitt
Department of Economics, University of Warwick
1
2009
Importance sampling squared for Bayesian inference in latent variable models
Minh-Ngoc Tran , Marcel Scharth , Michael K Pitt , Robert Kohn
arXiv preprint arXiv:1309.3339
55
2013
On the existence of moments for high dimensional importance sampling
Michael K Pitt , Minh-Ngoc Tran , Marcel Scharth , Robert Kohn
arXiv preprint arXiv:1307.7975
6
2013
Bayesian inference for latent factor GARCH models
Michael K Pitt , Jamie Hall , Robert Kohn
arXiv preprint arXiv:1507.01179
3
2015
Dynamic models using score copula innovations
Landan Zhang , Michael K Pitt , Robert Kohn
arXiv preprint arXiv:2104.00997
1
2021
Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals
Minh-Ngoc Tran , Michael K Pitt , Robert Kohn
Statistics and Computing
14
2013
Likelihood analysis of non-Gaussian measurement time series
Neil Shephard , Michael K Pitt
Biometrika 84 ( 3) 653 -667
886
1997
Time varying covariances: a factor stochastic volatility approach
Michael K Pitt , Neil Shephard
Bayesian statistics 6 547 -570
245
1999
Likelihood based inference for observed and partially observed diffusions
Siddhartha Chib , Michael K Pitt , Neil Shephard
Preprint
1
2003
2010
Time Varying Covariances: A Factor Stochastic Volatility Approach (with discussion
Neil Shephard , Michael K Pitt
University of Oxford, Department of Economics Economics Series Working Papers ( 1998-W05)
1998
Bayesian inference for non-Gaussian state space model using simulation.
Michael K Pitt
University of Oxford
1997
State space modelling and simulation filter methods
Michael K Pitt , Neil Shephard