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Cambridge Language Sciences

Interdisciplinary Research Centre
 

Professor John Aston

Research

My interests include all areas of Applied Statistics but particularly Statistical Neuroimaging and Statistical Linguistics. I also have an active collaboration with the Office for National Statistics. I have methodological interests amongst other things in Functional Data Analysis, Time Series Analysis, Image Analysis, Changepoint Analysis, and Spatial-Temporal Statistics.

Publications (from Symplectic)

Journal articles

2022 (Accepted for publication)

  • Jiang, C-R., Lila, E., Aston, J. and Wang, J-L., 2022 (Accepted for publication). Eigen-Adjusted Functional Principal Component Analysis Journal of Computational and Graphical Statistics,
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    2022

  • Lila, E. and Aston, JAD., 2022. Functional random effects modeling of brain shape and connectivity The Annals of Applied Statistics, v. 16
    Doi: 10.1214/21-aoas1572
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    2020

  • Lila, E., Arridge, S. and Aston, JAD., 2020. Representation and reconstruction of covariance operators in linear inverse problems Inverse Problems, v. 36
    Doi: 10.1088/1361-6420/ab8713
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    2019 (Accepted for publication)

  • Lila, E. and Aston, JAD., 2019 (Accepted for publication). Statistical Analysis of Functions on Surfaces, With an Application to Medical Imaging Journal of the American Statistical Association,
    Doi: http://doi.org/10.1080/01621459.2019.1635479
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    2019

  • Debroux, N., Aston, J., Bonardi, F., Forbes, A., Le Guyader, C., Romanchikova, M. and Schonlieb, CB., 2019. A variational model dedicated to joint segmentation, registration, and atlas generation for shape analysis SIAM Journal on Imaging Sciences, v. 13
    Doi: http://doi.org/10.1137/19M1271907
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  • Tavakoli, S., Pigoli, D., Aston, JAD. and Coleman, JS., 2019. Rejoinder for “A Spatial Modeling Approach for Linguistic Object Data: Analyzing Dialect Sound Variations Across Great Britain” Journal of the American Statistical Association, v. 114
    Doi: http://doi.org/10.1080/01621459.2019.1655931
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  • Pascoal, TA., Mathotaarachchi, S., Kang, MS., Mohaddes, S., Shin, M., Park, AY., Parent, MJ., Benedet, AL., Chamoun, M., Therriault, J., Hwang, H., Cuello, AC., Misic, B., Soucy, J-P., Aston, JAD., Gauthier, S. and Rosa-Neto, P., 2019. Aβ-induced vulnerability propagates via the brain's default mode network. Nat Commun, v. 10
    Doi: http://doi.org/10.1038/s41467-019-10217-w
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  • Tavakoli, S., Pigoli, D., Aston, JAD. and Coleman, JS., 2019. A Spatial Modeling Approach for Linguistic Object Data: Analyzing Dialect Sound Variations Across Great Britain Journal of the American Statistical Association, v. 114
    Doi: http://doi.org/10.1080/01621459.2019.1607357
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  • Kashlak, AB., Aston, JAD. and Nickl, R., 2019. Inference on covariance operators via concentration inequalities: K-sample tests, classification, and clustering via rademacher complexities Sankhya: The Indian Journal of Statistics, v. 81A
    Doi: http://doi.org/10.1007/s13171-018-0143-9
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  • Ward, R., Choudhary, R., Heo, Y. and Aston, J., 2019. A data-centric bottom up model for generation of stochastic internal load profiles based on space-use type Journal of Building Performance Simulation,
    Doi: http://doi.org/10.1080/19401493.2019.1583287
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  • Olszowy, W., Aston, J., Rua, C. and Williams, GB., 2019. Accurate autocorrelation modeling substantially improves fMRI reliability. Nat Commun, v. 10
    Doi: http://doi.org/10.1038/s41467-019-09230-w
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  • Olszowy, W., Aston, J., Rua, C. and Williams, GB., 2019. Publisher Correction: Accurate autocorrelation modeling substantially improves fMRI reliability. Nat Commun, v. 10
    Doi: http://doi.org/10.1038/s41467-019-09619-7
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    2018 (Accepted for publication)

  • Wang, Y., Dai, B., Hua, G., Aston, JAD. and Wipf, D., 2018 (Accepted for publication). Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers IEEE Journal on Selected Topics in Signal Processing,
    Doi: http://doi.org/10.1109/JSTSP.2018.2876995
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    2018

  • Dai, B., Wang, Y., Aston, J., Hua, G. and Wipf, D., 2018. Connections with robust PCA and the role of emergent sparsity in variational autoencoder models Journal of Machine Learning Research, v. 19
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    2017 (Accepted for publication)

  • Aston, J., Autin, F., Claeskens, G., Freyermuth, J-M. and Pouet, C., 2017 (Accepted for publication). Minimax optimal procedures for testing the structure of multidimensional functions Applied and Computational Harmonic Analysis,
    Doi: http://doi.org/10.1016/j.acha.2017.05.003
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  • Aston, JAD., Pigoli, D., Hadjipantelis, P. and Coleman, J., 2017 (Accepted for publication). The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages Journal of the Royal Statistical Society. Series C: Applied Statistics, v. 67
    Doi: http://doi.org/10.1111/rssc.12258
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    2017

  • Sayal, H., Aston, JAD., Elliott, D. and Ombao, H., 2017. An Introduction to Applications of Wavelet Benchmarking with Seasonal Adjustment Journal of the Royal Statistical Society Series A: Statistics in Society, v. 180
    Doi: 10.1111/rssa.12241
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  • Shiers, N., Aston, JAD., Smith, JQ. and Coleman, JS., 2017. Gaussian tree constraints applied to acoustic linguistic functional data Journal of Multivariate Analysis, v. 154
    Doi: http://doi.org/10.1016/j.jmva.2016.09.015
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  • Aston, JAD., Pigoli, D. and Tavakoli, S., 2017. Tests for separability in nonparametric covariance operators of random surfaces Annals of Statistics, v. 45
    Doi: http://doi.org/10.1214/16-AOS1495
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  • Tirlea, MA., Tavakoli, S., Pigoli, D. and Aston, JAD., 2017. A ticklish problem: Studying accent variation from speech recordings Significance, v. 14
    Doi: http://doi.org/10.1111/j.1740-9713.2017.01019.x
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  • Lindsten, F., Johansen, AM., Naesseth, CA., Kirkpatrick, B., Schön, TB., Aston, JAD. and Bouchard-Côté, A., 2017. Divide-and-Conquer With Sequential Monte Carlo Journal of Computational and Graphical Statistics, v. 26
    Doi: http://doi.org/10.1080/10618600.2016.1237363
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    2016

  • Shiers, N., Zwiernik, P., Aston, JAD. and Smith, JQ., 2016. The correlation space of Gaussian latent tree models and model selection without fitting Biometrika, v. 103
    Doi: http://doi.org/10.1093/biomet/asw032
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  • Jiang, C-R., Aston, JAD. and Wang, J-L., 2016. A Functional Approach to Deconvolve Dynamic Neuroimaging Data. J Am Stat Assoc, v. 111
    Doi: http://doi.org/10.1080/01621459.2015.1060241
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  • Lila, E., Aston, JAD. and Sangalli, LM., 2016. Smooth Principal Component Analysis over two-dimensional manifolds with an application to neuroimaging The Annals of Applied Statistics, v. 10
    Doi: 10.1214/16-aoas975
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  • Belavkin, RV., Channon, A., Aston, E., Aston, J., Krašovec, R. and Knight, CG., 2016. Monotonicity of fitness landscapes and mutation rate control. J Math Biol, v. 73
    Doi: http://doi.org/10.1007/s00285-016-0995-3
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  • Zhou, Y., Johansen, AM. and Aston, JAD., 2016. Toward Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach Journal of Computational and Graphical Statistics, v. 25
    Doi: http://doi.org/10.1080/10618600.2015.1060885
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    2015

  • Hadjipantelis, PZ., Aston, JAD., Müller, HG. and Evans, JP., 2015. Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese. J Am Stat Assoc, v. 110
    Doi: http://doi.org/10.1080/01621459.2015.1006729
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  • Nam, CFH., Aston, JAD., Eckley, IA. and Killick, R., 2015. The uncertainty of storm season changes: Quantifying the uncertainty of autocovariance changepoints Technometrics, v. 57
    Doi: http://doi.org/10.1080/00401706.2014.902776
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    2014

  • Krašovec, R., Belavkin, RV., Aston, JA., Channon, A., Aston, E., Rash, BM., Kadirvel, M., Forbes, S. and Knight, CG., 2014. Where antibiotic resistance mutations meet quorum-sensing. Microb Cell, v. 1
    Doi: http://doi.org/10.15698/mic2014.07.158
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  • Krašovec, R., Belavkin, RV., Aston, JAD., Channon, A., Aston, E., Rash, BM., Kadirvel, M., Forbes, S. and Knight, CG., 2014. Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell-cell interactions. Nat Commun, v. 5
    Doi: http://doi.org/10.1038/ncomms4742
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  • Aston, JAD., 2014. Comments on: Extensions of some classical methods in change point analysis DISCUSSION TEST, v. 23
    Doi: http://doi.org/10.1007/s11749-014-0369-3
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  • Nam, CFH., Aston, JAD. and Johansen, AM., 2014. Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model Annals of the Institute of Statistical Mathematics, v. 66
    Doi: http://doi.org/10.1007/s10463-014-0450-4
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  • Pigoli, D., Aston, JAD., Dryden, IL. and Secchi, P., 2014. Distances and inference for covariance operators Biometrika, v. 101
    Doi: http://doi.org/10.1093/biomet/asu008
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  • Aston, JAD. and Kirch, C., 2014. Efficiency of change point tests in high dimensional settings
  • Minas, G., Aston, JAD. and Stallard, N., 2014. Adaptive Multivariate Global Testing. J Am Stat Assoc, v. 109
    Doi: http://doi.org/10.1080/01621459.2013.870905
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  • Hadjipantelis, PZ., Aston, JAD., Müller, HG. and Moriarty, J., 2014. Analysis of spike train data: A multivariate mixed effects model for phase and amplitude Electronic Journal of Statistics,
    Doi: http://doi.org/10.1214/14-EJS865E
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  • Hall, AJ., Chappell, MJ., Aston, JAD. and Ward, SA., 2014. Reprint of "Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin". Comput Methods Programs Biomed, v. 114
    Doi: http://doi.org/10.1016/j.cmpb.2013.12.001
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    2013

  • Martin, DEK. and Aston, JAD., 2013. Distribution of Statistics of Hidden State Sequences Through the Sum-Product Algorithm Methodology and Computing in Applied Probability, v. 15
    Doi: http://doi.org/10.1007/s11009-012-9289-4
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  • Hall, AJ., Chappell, MJ., Aston, JAD. and Ward, SA., 2013. Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin. Comput Methods Programs Biomed, v. 112
    Doi: http://doi.org/10.1016/j.cmpb.2013.05.010
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  • Zhou, Y., Aston, JAD. and Johansen, AM., 2013. Bayesian model comparison for compartmental models with applications in positron emission tomography Journal of Applied Statistics, v. 40
    Doi: http://doi.org/10.1080/02664763.2013.772569
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  • Hindrayanto, I., Aston, JAD., Koopman, SJ. and Ooms, M., 2013. Modelling trigonometric seasonal components for monthly economic time series Applied Economics, v. 45
    Doi: http://doi.org/10.1080/00036846.2012.690937
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  • Sorrentino, A., Johansen, AM., Aston, JAD., Nichols, TE. and Kendall, WS., 2013. Dynamic filtering of static dipoles in magnetoencephalography Annals of Applied Statistics, v. 7
    Doi: http://doi.org/10.1214/12-AOAS611
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    2012

  • Hadjipantelis, PZ., Aston, JAD. and Evans, JP., 2012. Characterizing fundamental frequency in Mandarin: a functional principal component approach utilizing mixed effect models. J Acoust Soc Am, v. 131
    Doi: http://doi.org/10.1121/1.4714345
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  • Nam, CFH., Aston, JAD. and Johansen, AM., 2012. Quantifying the uncertainty in change points Journal of Time Series Analysis, v. 33
    Doi: http://doi.org/10.1111/j.1467-9892.2011.00777.x
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  • Aston, JAD. and Kirch, C., 2012. Detecting and estimating changes in dependent functional data Journal of Multivariate Analysis, v. 109
    Doi: http://doi.org/10.1016/j.jmva.2012.03.006
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  • Aston, JAD., Peng, JY. and Martin, DEK., 2012. Implied distributions in multiple change point problems Statistics and Computing, v. 22
    Doi: http://doi.org/10.1007/s11222-011-9268-6
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  • Aston, JAD. and Kirch, C., 2012. Evaluating stationarity via change-point alternatives with applications to fMRI data Annals of Applied Statistics, v. 6
    Doi: http://doi.org/10.1214/12-AOAS565
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  • Vavoulis, DV., Straub, VA., Aston, JAD. and Feng, J., 2012. A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons. PLoS Comput Biol, v. 8
    Doi: http://doi.org/10.1371/journal.pcbi.1002401
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    2011

  • Peng, JY. and Aston, JAD., 2011. The state space models toolbox for MATLAB Journal of Statistical Software, v. 41
    Doi: http://doi.org/10.18637/jss.v041.i06
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  • Chi, WC., Lee, WHK., Aston, JAD., Lin, CJ. and Liu, CC., 2011. Inversion of ground-motion data from a seismometer array for rotation using a modification of Jaeger's method Bulletin of the Seismological Society of America, v. 101
    Doi: http://doi.org/10.1785/0120100204
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    2010

  • Cheng, PE., Liou, M. and Aston, JAD., 2010. Likelihood ratio tests with three-way tables Journal of the American Statistical Association, v. 105
    Doi: http://doi.org/10.1198/jasa.2010.tm09061
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  • Aston, JAD., Chiou, JM. and Evans, JP., 2010. Linguistic pitch analysis using functional principal component mixed effect models Journal of the Royal Statistical Society. Series C: Applied Statistics, v. 59
    Doi: http://doi.org/10.1111/j.1467-9876.2009.00689.x
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  • Wang, RH., Aston, JAD. and Fuh, CD., 2010. The Role of Additional Information in Option Pricing: Estimation Issues for the State Space Model Computational Economics, v. 36
    Doi: http://doi.org/10.1007/s10614-010-9240-0
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  • Evans, J., Chu, M-N., Aston, JAD. and Su, C-Y., 2010. Linguistic and human effects on F(0) in a tonal dialect of Qiang. Phonetica, v. 67
    Doi: http://doi.org/10.1159/000319380
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    2009

  • Lee, J-D., Su, H-R., Cheng, PE., Liou, M., Aston, JAD., Tsai, AC. and Chen, C-Y., 2009. MR image segmentation using a power transformation approach. IEEE Trans Med Imaging, v. 28
    Doi: http://doi.org/10.1109/TMI.2009.2012896
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  • Jiang, C-R., Aston, JAD. and Wang, J-L., 2009. Smoothing dynamic positron emission tomography time courses using functional principal components. Neuroimage, v. 47
    Doi: http://doi.org/10.1016/j.neuroimage.2009.03.051
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  • Liou, M., Su, H-R., Savostyanov, AN., Lee, J-D., Aston, JAD., Chuang, C-H. and Cheng, PE., 2009. Beyond p-values: averaged and reproducible evidence in fMRI experiments. Psychophysiology, v. 46
    Doi: http://doi.org/10.1111/j.1469-8986.2008.00780.x
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    2008

  • Aston, JAD., Brown, EN., Loh, WL., Worsley, KJ. and Wu, Y., 2008. At the interface of statistics and brain science Statistica Sinica, v. 18
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  • Cheng, PE., Liou, M., Aston, JAD. and Tsai, AC., 2008. Information identities and testing hypotheses: Power analysis for contingency tables Statistica Sinica, v. 18
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  • Peng, J-Y., Aston, JAD., Gunn, RN., Liou, C-Y. and Ashburner, J., 2008. Dynamic positron emission tomography data-driven analysis using sparse Bayesian learning. IEEE Trans Med Imaging, v. 27
    Doi: http://doi.org/10.1109/TMI.2008.922185
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  • Aston, JAD., Fuh, CD. and Luo, SF., 2008. Discussion on "Is average run length to false alarm always an informative criterion?" by Yajun Mei Sequential Analysis, v. 27
    Doi: http://doi.org/10.1080/07474940802445899
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  • Martin, DEK. and Aston, JAD., 2008. Waiting time distribution of generalized later patterns Computational Statistics and Data Analysis, v. 52
    Doi: http://doi.org/10.1016/j.csda.2008.04.019
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    2007

  • Peng, J-Y. and Aston, JAD., 2007. The SSM Toolbox for Matlab
  • Aston, JAD., 2007. Modeling macroeconomic time series via heavy tailed distributions IMS Lecture Notes Monograph Series, v. 52
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  • Aston, JAD. and Martin, DEK., 2007. Distributions associated with general runs and patterns in hidden Markov models Annals of Applied Statistics, v. 1
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    2006

  • Liou, M., Su, H-R., Lee, J-D., Aston, JAD., Tsai, AC. and Cheng, PE., 2006. A method for generating reproducible evidence in fMRI studies. Neuroimage, v. 29
    Doi: http://doi.org/10.1016/j.neuroimage.2005.08.015
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  • Turkheimer, FE., Aston, JAD., Asselin, M-C. and Hinz, R., 2006. Multi-resolution Bayesian regression in PET dynamic studies using wavelets. Neuroimage, v. 32
    Doi: http://doi.org/10.1016/j.neuroimage.2006.03.002
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    2005

  • Aston, JAD. and Martin, DEK., 2005. Waiting time distributions of competing patterns in higher-order Markovian sequences Journal of Applied Probability, v. 42
    Doi: http://doi.org/10.1239/jap/1134587810
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  • Aston, JAD., Gunn, RN., Hinz, R. and Turkheimer, FE., 2005. Wavelet variance components in image space for spatiotemporal neuroimaging data. Neuroimage, v. 25
    Doi: http://doi.org/10.1016/j.neuroimage.2004.10.037
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    2004

  • Turkheimer, FE., Aston, JAD. and Cunningham, VJ., 2004. On the logic of hypothesis testing in functional imaging. Eur J Nucl Med Mol Imaging, v. 31
    Doi: http://doi.org/10.1007/s00259-003-1387-7
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    2003

  • Turkheimer, FE., Hinz, R., Gunn, RN., Aston, JAD., Gunn, SR. and Cunningham, VJ., 2003. Rank-shaping regularization of exponential spectral analysis for application to functional parametric mapping. Phys Med Biol, v. 48
    Doi: http://doi.org/10.1088/0031-9155/48/23/002
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    2000

  • Turkheimer, FE., Brett, M., Aston, JA., Leff, AP., Sargent, PA., Wise, RJ., Grasby, PM. and Cunningham, VJ., 2000. Statistical modeling of positron emission tomography images in wavelet space. J Cereb Blood Flow Metab, v. 20
    Doi: http://doi.org/10.1097/00004647-200011000-00011
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    Theses / dissertations

    2021 (No publication date)

  • Goucher, AP., 2021 (No publication date). Topological and geometric inference of data
    • Datasets

    2017 (No publication date)

  • Aston, J., Pigoli, D., Hadjipantelis, P. and Coleman, J., 2017 (No publication date). Research data supporting "The statistical analysis of acoustic phonetic data:exploring differences between spoken Romance languages"
    Doi: http://doi.org/10.17863/CAM.17139
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    Conference proceedings

    2017

  • Pascoal, TA., Mathotaarachchi, S., Kang, MS., Shin, M., Park, AY., Parent, M., Ng, KP., Soucy, J-P., Aston, JAD., Cuello, C., Gauthier, S. and Rosa-Neto, P., 2017. Global and regional amyloid-β effects on default model network drive progression to AD dementia JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, v. 37
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  • Wang, Y., Dai, B., Hua, G., Aston, J. and Wipf, D., 2017. Green generative modeling: Recycling dirty data using recurrent variational autoencoders Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017,
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    2012

  • Hall, AJ., Chappell, MJ., Aston, JAD. and Ward, SA., 2012. Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin IFAC Proceedings Volumes (IFAC-PapersOnline), v. 45
    Doi: http://doi.org/10.3182/20120829-3-HU-2029.00039
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  • Zhou, Y., Johansen, AM. and Aston, JAD., 2012. Bayesian model comparison via path-sampling sequential Monte Carlo 2012 IEEE Statistical Signal Processing Workshop, SSP 2012,
    Doi: http://doi.org/10.1109/SSP.2012.6319672
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    2011

  • Peng, JY., Aston, JAD. and Liou, CY., 2011. Modeling time series and sequences using Markov chain embedded finite automata International Journal of Innovative Computing, Information and Control, v. 7
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    2009

  • Martin, DEK. and Aston, JAD., 2009. Exact distribution of statistics of hidden state sequences via message passing in factor graphs AIP Conference Proceedings, v. 1127
    Doi: http://doi.org/10.1063/1.3146186
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    2008

  • Su, HR., Liou, M., Cheng, PE., Aston, JAD. and Lai, SH., 2008. Reproducibility analysis of event-related fMRI experiments using Laguerre polynomials Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 4984 LNCS
    Doi: http://doi.org/10.1007/978-3-540-69158-7_14
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    2006

  • Aston, JAD., Turkheimer, FE. and Brett, M., 2006. HBM functional imaging analysis contest data analysis in wavelet space. Hum Brain Mapp, v. 27
    Doi: http://doi.org/10.1002/hbm.20244
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    Professor of Statistics, Statistical Laboratory
    Currently on secondment as Chief Scientific Adviser to the Home Office
    Departments and institutes: 
    Department of Pure Mathematics and Mathematical Statistics
    Professor John Aston

    Affiliations

    Classifications: 
    Academic Staff

    Study at Cambridge

    About the University

    Research at Cambridge