I am a Reader in the Department of Mathematics, Imperial College London.

My research interests are primarily in statistical theory, particularly the implications of and means of inducing population-level sparsity, with a view to achieving reconciliation between low-dimensional Fisherian foundations and modern high-dimensional problems. See Battey (2023) for a summary of some of my work in this vein.


h.battey@imperial.ac.uk




Research funding

EPSRC Early Career Research Fellowship, Oct 2020 - Jan 2026.
Theoretical foundations of inference in the presence of a large number of nuisance parameters.
EPSRC Postdoctoral Research Fellowship, Jan 2017 - Jan 2020.
Inference for functions of large covariance matrices.

Editorial affiliations

Biometrika: Deputy Editor, 2024--
Biometrika: Associate Editor, 2020--2024.
Information and Inference: Associate Editor, 2022--2024.
J. Amer. Statist. Assoc.: Associate Editor, 2023--
J. R. Statist. Soc. Ser. B: Associate Editor, 2019--2024.
Member and Honorary Secretary of the Research Section Committee (editorial board for Series B discussion papers): 2018--2024
(Honorary Secretary 2022--2024).

PhD students

Jakub Rybak (2021-- )
Rebecca Lewis (2019--23)
Henrique Helfer Hoeltgebaum (informal supervision, 2018--19)

Pre-doctoral research supervision

Tung Pham (Undergraduate Research Opportunities Programme 2023)
Su Hyeong Lee (Undergraduate Research Opportunities Programme 2020)
Seyeon Kim (MMath research disseration 2019/20)
Joanna Lada (Mary Lister McCammon scholar 2019)
Rebecca Lewis (Undergraduate Research Opportunities Programme 2018)
Jakub Rybak (MSc research dissertation 2017/18)
Edoardo Lisi (BSc research dissertation 2016/17)
Aarti Jhoke (MMath research dissertation 2015/16)

Biographical outline

2016-present: Lecturer then Reader, Imperial College London, UK
2014-2016: Research Fellow, Princeton University, USA
2011-2014: Brunel Fellow in Statistics, University of Bristol, UK
2008-2011: PhD, University of Cambridge, UK

Fellow of the Institute of Mathematical Statistics (conferred 2023).

Discussion contributions and commissioned material

Battey, H. S. (2024)
D. R. Cox: aspects of scientific inference.
J. R. Statist. Soc. Ser. A (invited issue), to appear.

Battey, H. S. (2024)
Discussion of `Parameterizing and simulating from causal models' by Evans and Didelez.
J. R. Statist. Soc. Ser. B, to appear.

Battey, H. S. (2023)
Inducement of population-level sparsity.
Canadian J. Statist., 51, 760-768.
(Festschrift in honour of Nancy Reid)

Battey, H. S. (2023)
D. R. Cox: extracts from a memorial lecture.
Harvard Data Science Review, 5 (2).

Battey, H. S. (2022)
D. R. Cox Memorial Lecture.
JSM Proceedings, Washington DC, 635-639.

Battey, H. S. (2022)
Discussion of `Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes.
J. R. Statist. Soc. Ser. B, 84, 696-698.

Battey, H. S. and Reid, N. (2022)
Obituary: David Cox
IMS Bulletin, 51 (3), 14-16.

Battey, H. S. and Cox, D. R. (2022)
Aspects of non-standard multivariate analysis.
J. Multivar. Analysis (invited issue), 188, 104810.


A few preprints

Rybak, J., Battey, H. S. and Bharath, K. (2024)
Regression graphs and sparsity-inducing reparametrisations.
arXiv:2402.09112.

Battey, H. S. and Reid, N. (2024)
On the role of parametrisation in models with a misspecified nuisance component.
arXiv:2402.05708.

Lewis, R. and Battey, H. S. (2023)
Cox reduction and confidence sets of models: a theoretical elucidation.
arXiv:2302.12627.


Peer-reviewed publications

Lewis, R. and Battey, H. S. (2024)
On inference in high-dimensional logistic regression models with separated data.
Biometrika, to appear.

Battey, H. S. and McCullagh, P. (2024)
An anomaly arising in the analysis of processes with more than one source of variability.
Biometrika, to appear.

Battey, H. S., Cox, D. R. and Lee, S. (2024)
On partial likelihood and the construction of factorisable transformations.
Information Geometry, 7, 9-28.

Ward, S., Battey, H. S. and Cohen, E.A.K. (2023)
Estimation of the intensity function of a spatial point process on a Riemannian manifold.
Biometrika, 110, 1009-1021.

Battey, H. S. and Reid, N. (2023)
On inference in high-dimensional regression.
J. R. Statist. Soc. Ser. B, 85, 149-175. [matlab implementation]

Battey, H. S. and Cox, D. R. (2023)
Missing observations in regression: a conditional approach.
Royal Society Open Science, 10, 220267. [matlab implementation]

Battey, H. S. and Cox, D. R. (2022)
Some perspectives on inference in high dimensions.
Statistical Science, 37, 110-122.

Tan, K. M., Battey, H. S. and Zhou, W-X. (2022)
Communication-constrained quantile regression with statistical guarantees.
J. Machine Learning Research, 23, 12456--12516.

Rybak, J. and Battey, H. S. (2021)
Sparsity induced by covariance transformation: some deterministic and probabilistic results.
Proc. Roy. Soc. Lond. A (Math. Phys. Eng. Sci.), 477, 20200756.

Battey, H. S. (2021)
A note on the analytic approximation of exceedance probabilities in heterogeneous populations.
Statist. Probab. Lett., 179, 109215.

Nieto-Reyes, A. and Battey, H. S. (2021)
A topologically valid construction of depth for functional data.
J. Multivariate Analysis, 184, 104738.

Battey, H. S. and Cox, D. R. (2020)
High-dimensional nuisance parameters: an example from parametric survival analysis.
Information Geometry, 3, 119-148.

Battey, H. S. (2019)
On sparsity scales and covariance matrix transformations.
Biometrika, 106, 605-617.

Battey, H. S., Cox, D. R. and Jackson, M. V. (2019)
On the linear in probability model for binary data.
Royal Society Open Science, 6, 190067. [matlab implementation]

Hoeltgebaum, H. H. and Battey, H. S. (2019)
HCmodelSets: An R package for specifying sets of well-fitting models in high dimensions.
The R Journal, 11, 370-379.

Battey, H. S. and Cox, D. R. (2018)
Large numbers of explanatory variables: a probabilistic assessment.
Proc. Roy. Soc. Lond. A (Math. Phys. Eng. Sci.), 474, 20170631. [software]

Avella, M., Battey, H. S., Fan, J. and Li, Q. (2018)
Robust estimation of high-dimensional covariance and precision matrices.
Biometrika, 105, 271-284.

Battey, H. S., Fan, J., Liu, Lu and Zhu, Z. (2018)
Distributed testing and estimation in sparse high dimensional models.
Ann. Statist., 46, 1352-1382.

Cox, D. R. and Battey, H. S. (2017)
Large numbers of explanatory variables, a semi-descriptive analysis.
Proc. Nat. Acad. Sci., 114 (32), 8592-8595. [software].

Battey, H. S. (2017)
Eigen structure of a new class of structured covariance and inverse covariance matrices.
Bernoulli, 23, 3166-3177.

Nieto-Reyes, A. and Battey, H. S. (2016)
A topologically valid definition of depth for functional data.
Statistical Science, 31 61-79.

Battey, H. S., Feng, Q. and Smith, R. J. (2016)
Improving confidence set estimation when parameters are weakly identified.
Statist. Probab. Lett., 118 117-123.

Battey, H. S. and Linton, O. B. (2014)
Nonparametric estimation of multivariate elliptic densities via finite mixture sieves.
J. Multivar. Analysis, 123 43-67.

Battey, H. S. and Sancetta, A. (2013)
Conditional estimation for dependent functional data.
J. Multivar. Analysis, 120 1-17.



Peer-reviewed publications in other fields

Beale, N., Gunton, R., Bashe, K., Battey, H. S. and MacKay, R. S. (2024).
Dynamics of value-tracking.
Journal of Dynamics and Games, to appear.

Castagno, S. and five others including Battey, H. S. (2021)
Seizure outcomes of temporoparietooccipital and frontal disconnection in children with drug-resistant epilepsy.
Epilepsy Research, 177, 106769.

Beale, N., Battey, H. S., Davison, A. C. and MacKay, R. S. (2020)
An unethical optimization principle.
Royal Society Open Science, 7, 200462.

Beale, N., Rand, D., Battey, H., Croxson, May, R. M., Nowak, M. A. (2011)
Individual versus systemic risk and the Regulator's Dilemma.
Proc. Nat. Acad. Sci., 108 (31) 12647-12652