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Results for Statistics and Probability

Publications & Outputs

  1. Authors’ reply to the Discussion of ‘Automatic Change-Point Detection in Time Series via Deep Learning’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’

    Li, J., Fearnhead, P., Fryzlewicz, P. & Wang, T., 29/01/2024, (E-pub ahead of print) In: Journal of the Royal Statistical Society: Series B (Statistical Methodology).

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  2. Tamás P. Papp, Paul Fearnhead and Chris Sherlock's contribution to the discussion of “the Discussion Meeting on Probabilistic and statistical aspects of machine learning”

    Papp, T. P., Fearnhead, P. & Sherlock, C., 17/01/2024, (E-pub ahead of print) In: Journal of the Royal Statistical Society: Series B (Statistical Methodology).

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  3. Seconder of the vote of thanks and contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’

    Nemeth, C., 2/01/2024, (E-pub ahead of print) In: Journal of the Royal Statistical Society: Series B (Statistical Methodology).

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  4. Using biomarkers to allocate patients in a response-adaptive clinical trial

    Jackson, H., Bowen, S. & Jaki, T., 2/12/2023, In: Communications in Statistics: Simulation and Computation. 52, 12, p. 5946-5965 20 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  5. Seconder of the vote of thanks and contribution to the discussion of ‘the second discussion meeting on statistical aspects of the COVID-19 pandemic’

    Diggle, P. J., 30/09/2023, In: Journal of the Royal Statistical Society: Series A Statistics in Society. 71, 9, p. 5580 - 5594 14 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  6. Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods

    Whitehouse, M., Whiteley, N. & Rimella, L., 29/09/2023, In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 85, 4, p. 1173-1203 31 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  7. The importance of context in extreme value analysis with application to extreme temperatures in the USA and Greenland

    Clarkson, D., Eastoe, E. & Leeson, A., 31/08/2023, In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 72, 4, p. 829-843 15 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  8. High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling

    Cho, H., Maeng, H., Eckley, I. A. & Fearnhead, P., 14/07/2023, (Accepted/In press) In: Journal of the American Statistical Association. p. 1-13 13 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  9. Identifying irregular activity sequences: an application to passive household monitoring

    Gillam, J., Killick, R., Taylor, S., Heal, J. & Norwood, B., 30/06/2023, In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 72, 3, p. 519-543 25 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  10. Daniel Clarkson, Emma Eastoe and Amber Leeson's (Lancaster University) reply to the Discussion of ‘Statistical aspects of climate change’

    Clarkson, D., Eastoe, E. & Leeson, A., 28/06/2023, (E-pub ahead of print) In: Journal of the Royal Statistical Society: Series C (Applied Statistics).

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  11. Micro-Macro Changepoint Inference for Periodic Data Sequences

    Ushakova, A., Taylor, S. & Killick, R., 31/05/2023, In: Journal of Computational and Graphical Statistics. 32, 2, p. 684-695 12 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

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