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Professor Michael Pitt
Professor in Statistics
Research interests
- Mathematics
Biography
Michael Pitt completed his doctorate in 1997 at Nuffield College and the Department of Statistics at the University of Oxford. After a two year post-doctoral position in the Statistics division at Imperial College London, he joined the Economics department at the University of Warwick as an Assistant Professor (lecturer) in 1999 becoming Associate Professor in 2005. He has worked on a number of statistical and econometric problems. A particular area of interest is in particle filtering (also known as sequential Monte Carlo) applied to financial time series. He joined King's College London as a Professor in Statistics in the Department of Mathematics in January 2016.
Research interests
- Estimation of financial times models in discrete and continuous time
- Multivariate copula models
- The efficient implementation Markov chain Monte Carlo
- Sequential Monte Carlo (SMC) methods
Further information
Impact of Anatomical and Viability-Guided Completeness of Revascularization on Clinical Outcomes in Ischemic Cardiomyopathy
REVIVED-BCIS2 Investigators, 23 Jul 2024, In: Journal of the American College of Cardiology. 84, 4, p. 340-350 11 p.Research output: Contribution to journal › Article › peer-review
Large Sample Asymptotics of the Pseudo-Marginal Method
Pitt, M. K., Deligiannidis, G., Doucet, A. & SCHMON, S., 11 Jul 2020, (E-pub ahead of print) In: BIOMETRIKA. 2020, 14 p., asaa044.Research output: Contribution to journal › Article › peer-review
The Correlated Pseudo-Marginal Method
Deligiannidis, G., Doucet, A. & Pitt, M. K., Nov 2018, In: Journal of the Royal Statistical Society. Series B: Statistical Methodology. 80, 5, p. 839-870 23 p.Research output: Contribution to journal › Article › peer-review
Simulated likelihood inference for stochastic volatility models using continuous particle filtering
Pitt, M. K., Malik, S. & Doucet, A., Jun 2014, In: Annals of the Institute of Statistical Mathematics. 66, 3, p. 527–552 25 p.Research output: Contribution to journal › Article › peer-review
Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures
Tran, M.-N., Giordani, P., Mun, X., Kohn, R. & Pitt, M. K., 25 Sept 2013, In: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS. 23, 4, p. 1163-1178 15 p.Research output: Contribution to journal › Article › peer-review
Adaptive Metropolis–Hastings Sampling using Reversible Dependent Mixture Proposals
Tran, M.-N., Pitt, M. K. & Kohn, R., 1 Jan 2016, In: STATISTICS AND COMPUTING. 26, 1-2, p. 361–381 20 p.Research output: Contribution to journal › Article › peer-review
Bayesian inference for nonlinear structural time series models
Hall, J. H., Pitt, M. K. & kohn, R., Apr 2014, In: JOURNAL OF ECONOMETRICS. 179, 2, p. 99-111 12 p.Research output: Contribution to journal › Article › peer-review
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Doucet, A., Pitt, M., Deligiannidis, G. & Kohn, R., Jun 2015, In: BIOMETRIKA. 102, 2, p. 295-313 19 p.Research output: Contribution to journal › Article › peer-review
Research

Centre for Non-Equilibrium Science (CNES)
CNES acts as an international hub for cross-disciplinary research in non-equilibrium science.

Statistics
The group has research strengths in the design and analysis of experiments, time series and Markov chain Monte Carlo and sequential Monte Carlo methods.
Impact of Anatomical and Viability-Guided Completeness of Revascularization on Clinical Outcomes in Ischemic Cardiomyopathy
REVIVED-BCIS2 Investigators, 23 Jul 2024, In: Journal of the American College of Cardiology. 84, 4, p. 340-350 11 p.Research output: Contribution to journal › Article › peer-review
Large Sample Asymptotics of the Pseudo-Marginal Method
Pitt, M. K., Deligiannidis, G., Doucet, A. & SCHMON, S., 11 Jul 2020, (E-pub ahead of print) In: BIOMETRIKA. 2020, 14 p., asaa044.Research output: Contribution to journal › Article › peer-review
The Correlated Pseudo-Marginal Method
Deligiannidis, G., Doucet, A. & Pitt, M. K., Nov 2018, In: Journal of the Royal Statistical Society. Series B: Statistical Methodology. 80, 5, p. 839-870 23 p.Research output: Contribution to journal › Article › peer-review
Simulated likelihood inference for stochastic volatility models using continuous particle filtering
Pitt, M. K., Malik, S. & Doucet, A., Jun 2014, In: Annals of the Institute of Statistical Mathematics. 66, 3, p. 527–552 25 p.Research output: Contribution to journal › Article › peer-review
Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures
Tran, M.-N., Giordani, P., Mun, X., Kohn, R. & Pitt, M. K., 25 Sept 2013, In: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS. 23, 4, p. 1163-1178 15 p.Research output: Contribution to journal › Article › peer-review
Adaptive Metropolis–Hastings Sampling using Reversible Dependent Mixture Proposals
Tran, M.-N., Pitt, M. K. & Kohn, R., 1 Jan 2016, In: STATISTICS AND COMPUTING. 26, 1-2, p. 361–381 20 p.Research output: Contribution to journal › Article › peer-review
Bayesian inference for nonlinear structural time series models
Hall, J. H., Pitt, M. K. & kohn, R., Apr 2014, In: JOURNAL OF ECONOMETRICS. 179, 2, p. 99-111 12 p.Research output: Contribution to journal › Article › peer-review
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Doucet, A., Pitt, M., Deligiannidis, G. & Kohn, R., Jun 2015, In: BIOMETRIKA. 102, 2, p. 295-313 19 p.Research output: Contribution to journal › Article › peer-review
Research

Centre for Non-Equilibrium Science (CNES)
CNES acts as an international hub for cross-disciplinary research in non-equilibrium science.

Statistics
The group has research strengths in the design and analysis of experiments, time series and Markov chain Monte Carlo and sequential Monte Carlo methods.