I am a lecturer (roughly equivalent to assistant professor with tenure) in the Department of Mathematics, Imperial College London.

My research interests are primarily in statistical theory, particularly inference for interest parameters in the presence of a large number of nuisance parameters.

I am also on the research board of the Institute of Digital Molecular Design and Fabrication, founded by the Department of Chemistry and the Department of Chemical Engineering.


Editorial affiliations

Associate Editor for Biometrika
Associate Editor for Journal of the Royal Statistical Society, Series B
Member of the Research Section Committee of the Royal Statistical Society, responsible for handling Series B discussion papers.

Research grants

EPSRC Early Career Research Fellowship, Oct 2020 - Sep 2025.
Theoretical foundations of inference in the presence of a large number of nuisance parameters.
EPSRC Postdoctoral Research Fellowship, Jan 2017 - Dec 2019.
Inference for functions of large covariance matrices.

Biographical outline

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

PhD students

Rebecca Lewis
Jakub Rybak

Publications: statistical theory and applied probability

Battey, H. S. and Cox, D. R. (2021)
Some perspectives on inference and asymptotic analysis in high dimensions.
Statistical Science, to appear.

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

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.

Battey, H. S. and Cox, D. R. (2018)
Large numbers of explanatory variables: a probabilistic assessment.
Proc. R. Soc. Lond. A., 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. Supplementary file.

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 mixture sieves.
J. Multivariate Analysis, 123 43-67.

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

Publications: other

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

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.

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