Teaching

In 2022-2023, I will teach ST444 Computational Data Science in the Michaelmas term, and ST304 Time Series and Forecasting in the Lent term. Course materials can be found via Moodle. I will also teach a course on Time Series Analysis at the London Taught Course Centre (LTCC) in Spring 2023.

In the past, I had also lectured ST442 Time Series,  ST447 Data Analysis and Statistical Methods and had supervised ST498 Data Science Capstone Project and ST499 MSc Dissertation.  

 

Research

My current research interests include shape-constrained estimation, time series analysis, change-point detection and statistical computing. Here is a list of my publications:

 

Statistical Theory and Methodology:

• Feng, O. Y., Chen, Y., Han, Q., Carroll, R. J. and Samworth, R. J. (2022) Nonparametric, tuning-free estimation of S-shaped functions, Journal of Royal Statistical Society: Series B, 84, 1324-1352. (.pdf) The accompanying R package Sshaped, is available from CRAN.
  

• Chen, Y. (2021) Jump or Kink: Note on super-efficiency in segmented linear regression break-point estimation, Biometrika, 108, 215-222. (.pdf)
  

• Baranowski, R., Chen, Y. and Fryzlewicz, P. (2020) Ranking-based variable selection for high-dimensional data, Statistica Sinica, 30, 1485-1516. (.pdf) The methodology is implemented in the R package rbvs, available from CRAN.
  

• Yagi, D., Chen, Y., Johnson, A. L. and Kuosmanen, T. (2020) Shape constrained kernel-weighted least squares: estimating production functions for Chilean manufacturing industries, Journal of Business & Economic Statistics, 38, 43-54. (.pdf).
  

• Baranowski, R., Chen, Y. and Fryzlewicz, P. (2019) Narrowest-Over-Threshold detection of multiple change-points and change-point-like features, Journal of Royal Statistical Society: Series B, 81, 649–672. (.pdf) The accompanying R package not, short for narrowest-over-threshold, is available from CRAN.
  

• Chen, Y. and Samworth, R. J. (2016) Generalized additive and index models with shape constraints, Journal of Royal Statistical Society: Series B, 78, 729–754. (.pdf) The accompanying R package scar, short for shape constrained additive regression, is available from CRAN.
  

• Chen, Y. and Wellner, J. A. (2016) On convex least squares estimation when the truth is linear, Electronic Journal of Statistics, 10, 171-209. (.pdf)
  

• Chen, Y. (2015) Semiparametric time series models with log-concave innovations: maximum likelihood estimation and its consistency, Scandinavian Journal of Statistics, 42, 1-31. (.pdf)

• Chen, Y. and Samworth, R. J. (2013) Smoothed log-concave maximum likelihood estimation with applications, Statistica Sinica, 23, 1373-1398. (.pdf)

 

Statistical Software:

• Feng, O. Y., Chen, Y., Han, Q., Carroll, R. J. and Samworth, R. J. (2021) Sshaped, An R package for nonparametric, tuning-free estimation of an S-shaped function, version 0.99 available from CRAN.

• Anastasiou, A., Chen, Y., Cho, H., and Fryzlewicz, P. (2020) breakfast, A (developing) R package for multiple change-point detection/estimation (data segmentation), version 2.1 available from CRAN.

• Baranowski, R., Chen, Y. and Fryzlewicz, P. (2016) not, An R package for narrowest-over-threshold detection of multiple change-points and change-point-like features, version 1.0 available from CRAN.

• Chen, Y. and Samworth, R. J. (2014) scar, An R package for shape constrained additive regression, version 0.2-1 available from available from CRAN.

• Cule, M. L., Gramacy, R. B., Samworth, R. J. and Chen, Y. (2007) LogConcDEAD, An R package for log-concave density estimation in arbitrary dimensions, version 1.5-4 available from CRAN.

 

Invited Discussion:

• Chen, Y. and Shah, R. D. (2017) Discussion of Random projection ensemble classification by Cannings and Samworth, Journal of Royal Statistical Society: Series B, 79, 1003-1004.   

• Chen, Y., Shah, R. D. and Samworth, R. J. (2014) Discussion of Multiscale change point inference by Frick, Munk and Sieling, Journal of Royal Statistical Society: Series B, 76, 544-546.   

• Chen, Y. (2012) Discussion of Constructing summary statistics for approximate bayesian computation: semi-automatic ABC by Fearnhead and Prangle, Journal of the Royal Statistical Society: Series B, 74, 455.

• Chen, Y. (2010) Discussion of Maximum likelihood estimation of a multidimensional log-concave density by Cule, Samworth and Stewart, Journal of the Royal Statistical Society: Series B, 72, 590-593.

 

Interdisciplinary Publications:

• Makariou, D., Barrieu, P. and Chen, Y. (2021+) A random forest based approach for predicting spreads in the primary catastrophe bond market, Insurance: Mathematics and Economics, to appear. (.html)

• Ayorinde, J. O. O., Hamed, M., Goh, M. A., Summers, D. M., Dare, A., Chen, Y. and Saeb-Parsy, K. (2020) Development of an objective, standardized tool for surgical assessment of deceased donor kidneys: The Cambridge Kidney Assessment Tool, Clinical Transplantation, 34, e13781, 1-11. (.pdf)

• Kosmoliaptsis, V., Mallon, D. H., Chen, Y., Bolton, E. M., Bradley, J. A. and Taylor, C. J. (2016) Alloantibody responses after renal transplant failure can be better predicted by donor–recipient HLA amino acid sequence and physicochemical disparities than conventional HLA matching, American Journal of Transplantation, 16, 2139-2147. (.pdf)

• Hamed, M. O., Chen, Y., Pasea, L., Watson, C. J., Torpey, N., Bradley, A., Pettigrew, G. J. and Saeb-Parsy, K. (2015) Early graft loss after kidney transplantation: risk factors and consequences, American Journal of Transplantation, 15, 1632-1643. (.pdf)

• Kosmoliaptsis, V., Salji, M., Bardsley, V., Chen, Y., Thiru, S., Griffiths, M. H., Copley, H. C., Saeb-Parsy, K., Bradley, A., Torpey, N. and Pettigrew, G. J. (2014) Baseline donor chronic renal injury confers the same transplant survival disadvantage for DCD and DBD kidneys, American Journal of Transplantation, 15, 754-763. (.pdf)

• Ali, J. M., Davies, S. E., Brais, R. J., Randle, L. V., Klinck, J. R., Allison, M. E. D., Chen, Y., Pasea, L., Harper, S. F. J. and Pettigrew, G. J. (2015) Analysis of ischaemia/reperfusion injury in time-zero biopsies predicts liver allograft outcomes, Liver Transplantation, 21, 487-499. (.pdf)