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., 12/04/2024, In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 86, 2, p. 332-334 3 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

2. Automatic Change-Point Detection in Time Series via Deep Learning

   Li, J., Fearnhead, P., Fryzlewicz, P. & Wang, T., 12/04/2024, In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 86, 2, p. 273-285 13 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

3. 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., 12/04/2024, In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 86, 2, p. 327-328 2 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

4. Discussion on “The central role of the identifying assumption in population size estimation” by Serge Aleshin-Guendel, Mauricio Sadinle, and Jon Wakefield

   Whitehead, J., 31/03/2024, In: Biometrics. 80, 1, ujad031.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

5. Regime-based precipitation modeling: A spatio-temporal approach

   Euán, C., Sun, Y. & Reich, B. J., 19/02/2024, (Accepted/In press) In: Spatial Statistics. 100818.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

6. Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus

   Speyer, L. G., Murray, A. L. & Kievit, R., 14/02/2024, (E-pub ahead of print) In: Multivariate Behavioral Research. 18 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

7. 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/Magazine › Journal article › peer-review

8. 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/Magazine › Journal article › peer-review

9. 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/Magazine › Journal article › peer-review

10. 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/Magazine › Journal article › peer-review

11. 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/Magazine › Journal article › peer-review

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