Results for Statistics and Probability

Publications & Outputs

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

   Cho, H., Maeng, H., Eckley, I. A. & Fearnhead, P., 2/07/2024, In: Journal of the American Statistical Association. 119, 547, p. 2038-2050 13 p.

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

2. 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., 1/06/2024, In: Multivariate Behavioral Research. 59, 3, p. 620-637 18 p.

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

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

   Euán, C., Sun, Y. & Reich, B. J., 30/04/2024, In: Spatial Statistics. 60, 100818.

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

4. 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

5. 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

6. 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

7. 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

8. Effects of Allocation Method and Time Trends on Identification of the Best Arm in Multi-arm Trials

   Berry, L. R., Lorenzi, E., Berry, N. S., Crawford, A. M., Jacko, P. & Viele, K., 23/01/2024, In: Statistics in Biopharmaceutical Research. 16, 4, p. 512-525 14 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 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

10. Accounting for seasonality in extreme sea-level estimation

    D’Arcy, E., Tawn, J. A., Joly, A. & Sifnioti, D. E., 31/12/2023, In: Annals of Applied Statistics. 17, 4, p. 3500-3525 26 p.

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

11. 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

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