| Filtering via simulation: auxiliary particle filters MK Pitt, N Shephard Journal of the American Statistical Association 94 (446), 590-599, 1999 | 3451 | 1999 |
| Likelihood analysis of non-Gaussian measurement time series N Shephard, MK Pitt Biometrika 84 (3), 653-667, 1997 | 933 | 1997 |
| On some properties of Markov chain Monte Carlo simulation methods based on the particle filter MK Pitt, RS Silva, P Giordani, R Kohn Journal of Econometrics 171 (2), 134–151, 2012 | 402* | 2012 |
| Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator A Doucet, M Pitt, G Deligiannidis, R Kohn Biometrika 102 (2), 295–313, 2015 | 342 | 2015 |
| Particle filters for continuous likelihood evaluation and maximisation S Malik, MK Pitt Journal of Econometrics 165 (2), 190–209, 2011 | 327* | 2011 |
| Efficient Bayesian inference for Gaussian copula regression models M Pitt, D Chan, R Kohn Biometrika 93 (3), 537-554, 2006 | 310 | 2006 |
| Time varying covariances: a factor stochastic volatility approach MK Pitt, N Shephard Bayesian statistics 6, 547-570, 1999 | 243 | 1999 |
| The correlated pseudo-marginal method G Deligiannidis, A Doucet, MK Pitt Journal of the Royal Statistical Society, Series B, 2018 | 147 | 2018 |
| Auxiliary variable based particle filters MK Pitt, N Shephard Sequential Monte Carlo methods in practice, 273-293, 2001 | 143 | 2001 |
| Likelihood based inference for diffusion driven models. S Chib, M Pitt, N Shephard Nuffield College (University of Oxford), 2004 | 94* | 2004 |
| Analytic convergence rates and parameterization issues for the Gibbs sampler applied to state space models MK Pitt, N Shephard Journal of Time Series Analysis 20 (1), 63-85, 1999 | 77 | 1999 |
| Constructing first order stationary autoregressive models via latent processes MK Pitt, C Chatfield, SG Walker Scandinavian Journal of Statistics 29 (4), 657-663, 2002 | 72 | 2002 |
| Trade union decline and the distribution of wages in the UK: evidence from kernel density estimation BD Bell, MK Pitt Oxford Bulletin of Economics and Statistics 60 (4), 509-528, 1998 | 65 | 1998 |
| Constructing stationary time series models using auxiliary variables with applications MK Pitt, SG Walker Journal of the American Statistical Association 100 (470), 554-564, 2005 | 59* | 2005 |
| Simulated likelihood inference for stochastic volatility models using continuous particle filtering MK Pitt, S Malik, A Doucet Annals of the Institute of Statistical Mathematics 66 (3), 527-552, 2014 | 55* | 2014 |
| Importance sampling squared for Bayesian inference in latent variable models MN Tran, M Scharth, MK Pitt, R Kohn arXiv preprint arXiv:1309.3339, 2013 | 53 | 2013 |
| Bayesian inference for time series state space models P Giordani, M Pitt, R Kohn | 37 | 2011 |
| Large sample asymptotics of the pseudo-marginal method SM Schmon, G Deligiannidis, A Doucet, MK Pitt Biometrika 108 (1), 37–51, 2021 | 33 | 2021 |
| Bayesian inference for nonlinear structural time series models J Hall, MK Pitt, R Kohn Journal of Econometrics 179 (2), 99-111, 2014 | 19 | 2014 |
| Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals MN Tran, MK Pitt, R Kohn Statistics and Computing 26 (1-2), 361-381, 2016 | 13 | 2016 |
Neil ShephardFrank B. Baird Jr, Professor of Science, Dept of Economics & Dept of Statistics, Harvard UniversityVerified email at fas.harvard.edu
