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Adam Michael Johansen

Personal Details

First Name:Adam
Middle Name:Michael
Last Name:Johansen
Suffix:
RePEc Short-ID:pjo193
[This author has chosen not to make the email address public]
https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/johansen

Affiliation

Department of Statistics
University of Warwick

Coventry, United Kingdom
http://www.warwick.ac.uk/go/statistics
RePEc:edi:dswaruk (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Crucinio, Francesca R. & De Bortoli, Valentin & Doucet, Arnaud & Johansen, Adam M., 2024. "Solving a class of Fredholm integral equations of the first kind via Wasserstein gradient flows," Stochastic Processes and their Applications, Elsevier, vol. 173(C).
  2. Crucinio, Francesca R. & Johansen, Adam M., 2023. "Properties of marginal sequential Monte Carlo methods," Statistics & Probability Letters, Elsevier, vol. 203(C).
  3. Francesca R. Crucinio & Arnaud Doucet & Adam M. Johansen, 2023. "A Particle Method for Solving Fredholm Equations of the First Kind," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 937-947, April.
  4. Brown, Suzie & Jenkins, Paul A. & Johansen, Adam M. & Koskela, Jere, 2023. "Weak convergence of non-neutral genealogies to Kingman’s coalescent," Stochastic Processes and their Applications, Elsevier, vol. 162(C), pages 76-105.
  5. James Hodgson & Adam M. Johansen & Murray Pollock, 2022. "Unbiased Simulation of Rare Events in Continuous Time," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 2123-2148, September.
  6. Måns Unosson & Marco Brancaccio & Michael Hastings & Adam M Johansen & Bärbel Finkenstädt, 2021. "A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells," PLOS Computational Biology, Public Library of Science, vol. 17(12), pages 1-19, December.
  7. Angeli, Letizia & Grosskinsky, Stefan & Johansen, Adam M., 2021. "Limit theorems for cloning algorithms," Stochastic Processes and their Applications, Elsevier, vol. 138(C), pages 117-152.
  8. Murray Pollock & Paul Fearnhead & Adam M. Johansen & Gareth O. Roberts, 2020. "Quasi‐stationary Monte Carlo and the ScaLE algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1167-1221, December.
  9. Matthew Thorpe & Adam M. Johansen, 2018. "Pointwise convergence in probability of general smoothing splines," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 717-744, August.
  10. Pieralberto Guarniero & Adam M. Johansen & Anthony Lee, 2017. "The Iterated Auxiliary Particle Filter," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1636-1647, October.
  11. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.
  12. Christopher Nam & John Aston & Adam Johansen, 2014. "Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 553-575, June.
  13. Yan Zhou & John Aston & Adam Johansen, 2013. "Bayesian model comparison for compartmental models with applications in positron emission tomography," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(5), pages 993-1016.
  14. Christopher F. H. Nam & John A. D. Aston & Adam M. Johansen, 2012. "Quantifying the uncertainty in change points," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(5), pages 807-823, September.
  15. Johansen, Adam M., 2009. "SMCTC: Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i06).
  16. Johansen, Adam M. & Doucet, Arnaud, 2008. "A note on auxiliary particle filters," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1498-1504, September.
  17. Adam M. Johansen & Sumeetpal S. Singh & Arnaud Doucet & Ba-Ngu Vo, 2006. "Convergence of the SMC Implementation of the PHD Filte," Methodology and Computing in Applied Probability, Springer, vol. 8(2), pages 265-291, June.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Angeli, Letizia & Grosskinsky, Stefan & Johansen, Adam M., 2021. "Limit theorems for cloning algorithms," Stochastic Processes and their Applications, Elsevier, vol. 138(C), pages 117-152.

    Cited by:

    1. Cloez, Bertrand & Corujo, Josué, 2022. "Uniform in time propagation of chaos for a Moran model," Stochastic Processes and their Applications, Elsevier, vol. 154(C), pages 251-285.

  2. Murray Pollock & Paul Fearnhead & Adam M. Johansen & Gareth O. Roberts, 2020. "Quasi‐stationary Monte Carlo and the ScaLE algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1167-1221, December.

    Cited by:

    1. Gael M. Martin & David T. Frazier & Christian P. Robert, 2022. "Computing Bayes: From Then `Til Now," Monash Econometrics and Business Statistics Working Papers 14/22, Monash University, Department of Econometrics and Business Statistics.

  3. Pieralberto Guarniero & Adam M. Johansen & Anthony Lee, 2017. "The Iterated Auxiliary Particle Filter," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1636-1647, October.

    Cited by:

    1. Joshua Chan & Arnaud Doucet & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 18-12, National Graduate Institute for Policy Studies.
    2. Rutger Jan Lange, 2020. "Bellman filtering for state-space models," Tinbergen Institute Discussion Papers 20-052/III, Tinbergen Institute, revised 19 May 2021.
    3. Nicolas Chopin & Mathieu Gerber, 2017. "Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes," Working Papers 2017-35, Center for Research in Economics and Statistics.
    4. Patrick Leung & Catherine S. Forbes & Gael M Martin & Brendan McCabe, 2019. "Forecasting Observables with Particle Filters: Any Filter Will Do!," Monash Econometrics and Business Statistics Working Papers 22/19, Monash University, Department of Econometrics and Business Statistics.
    5. Patrick Leung & Catherine S. Forbes & Gael M. Martin & Brendan McCabe, 2016. "Data-driven particle Filters for particle Markov Chain Monte Carlo," Monash Econometrics and Business Statistics Working Papers 17/16, Monash University, Department of Econometrics and Business Statistics.
    6. Matti Vihola & Jouni Helske & Jordan Franks, 2020. "Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1339-1376, December.
    7. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.

  4. Yan Zhou & John Aston & Adam Johansen, 2013. "Bayesian model comparison for compartmental models with applications in positron emission tomography," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(5), pages 993-1016.

    Cited by:

    1. Peter Malave & Arkadiusz Sitek, 2015. "Bayesian analysis of a one-compartment kinetic model used in medical imaging," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(1), pages 98-113, January.

  5. Christopher F. H. Nam & John A. D. Aston & Adam M. Johansen, 2012. "Quantifying the uncertainty in change points," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(5), pages 807-823, September.

    Cited by:

    1. Sean Jewell & Paul Fearnhead & Daniela Witten, 2022. "Testing for a change in mean after changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1082-1104, September.
    2. Cho, Haeran & Kirch, Claudia, 2022. "Bootstrap confidence intervals for multiple change points based on moving sum procedures," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    3. Christopher Nam & John Aston & Adam Johansen, 2014. "Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 553-575, June.
    4. Donald E. K. Martin & John A. D. Aston, 2013. "Distribution of Statistics of Hidden State Sequences Through the Sum-Product Algorithm," Methodology and Computing in Applied Probability, Springer, vol. 15(4), pages 897-918, December.

  6. Johansen, Adam M., 2009. "SMCTC: Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i06).

    Cited by:

    1. Zhou, Yan, 2015. "vSMC: Parallel Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i09).
    2. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    3. Murray, Lawrence M., 2015. "Bayesian State-Space Modelling on High-Performance Hardware Using LibBi," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i10).

  7. Johansen, Adam M. & Doucet, Arnaud, 2008. "A note on auxiliary particle filters," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1498-1504, September.

    Cited by:

    1. Maciej Augustyniak & Mathieu Boudreault & Manuel Morales, 2018. "Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 165-188, March.
    2. Crucinio, Francesca R. & Johansen, Adam M., 2023. "Properties of marginal sequential Monte Carlo methods," Statistics & Probability Letters, Elsevier, vol. 203(C).
    3. Elmar Mertens & James M. Nason, 2018. "Inflation and professional forecast dynamics: an evaluation of stickiness, persistence, and volatility," BIS Working Papers 713, Bank for International Settlements.
    4. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.
    5. Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018. "Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model," DEA Working Papers 88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    6. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    7. Murray Pollock & Paul Fearnhead & Adam M. Johansen & Gareth O. Roberts, 2020. "Quasi‐stationary Monte Carlo and the ScaLE algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1167-1221, December.
    8. Yang, Yuan & Wang, Lu, 2015. "An Improved Auxiliary Particle Filter for Nonlinear Dynamic Equilibrium Models," Dynare Working Papers 47, CEPREMAP.

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