| A comparative review of dimension reduction methods in approximate Bayesian computation MGB Blum, MA Nunes, D Prangle, SA Sisson | 459 | 2013 |
| On optimal selection of summary statistics for approximate Bayesian computation MA Nunes, DJ Balding Statistical applications in genetics and molecular biology 9 (1), 2010 | 227 | 2010 |
| abctools: an R package for tuning approximate Bayesian computation analyses MA Nunes, D Prangle The R Journal, 2015 | 61 | 2015 |
| Generalised network autoregressive processes and the GNAR package M Knight, K Leeming, G Nason, M Nunes arXiv preprint arXiv:1912.04758, 2019 | 52 | 2019 |
| Adaptive lifting for nonparametric regression MA Nunes, MI Knight, GP Nason Statistics and Computing 16, 143-159, 2006 | 50 | 2006 |
| Spectral estimation for locally stationary time series with missing observations MI Knight, MA Nunes, GP Nason Statistics and Computing 22, 877-895, 2012 | 34 | 2012 |
| Modelling, detrending and decorrelation of network time series MI Knight, MA Nunes, GP Nason arXiv preprint arXiv:1603.03221, 2016 | 31 | 2016 |
| A wavelet lifting approach to long-memory estimation MI Knight, GP Nason, MA Nunes Statistics and Computing 27 (6), 1453-1471, 2017 | 24 | 2017 |
| Interpretable brain age prediction using linear latent variable models of functional connectivity RP Monti, A Gibberd, S Roy, M Nunes, R Lorenz, R Leech, T Ogawa, ... Plos one 15 (6), e0232296, 2020 | 22 | 2020 |
| A test of stationarity for textured images SL Taylor, IA Eckley, MA Nunes Technometrics 56 (3), 291-301, 2014 | 19 | 2014 |
| A wavelet-based approach for imputation in nonstationary multivariate time series RE Wilson, IA Eckley, MA Nunes, T Park Statistics and Computing 31 (2), 18, 2021 | 14 | 2021 |
| Long memory estimation for complex-valued time series MI Knight, MA Nunes Statistics and computing 29, 517-536, 2019 | 14 | 2019 |
| A Multiscale Test of Spatial Stationarity for Textured Images in R. MA Nunes, SL Taylor, IA Eckley R Journal 6 (1), 2014 | 12 | 2014 |
| Modelling time-varying first and second-order structure of time series via wavelets and differencing ET McGonigle, R Killick, MA Nunes Electronic Journal of Statistics 16 (2), 4398-4448, 2022 | 9 | 2022 |
| Trend locally stationary wavelet processes ET McGonigle, R Killick, MA Nunes Journal of Time Series Analysis 43 (6), 895-917, 2022 | 8 | 2022 |
| GNAR: Methods for fitting network time series models K Leeming, GP Nason, MA Nunes, MI Knight | 8 | 2019 |
| Multivariate locally stationary 2D wavelet processes with application to colour texture analysis SL Taylor, IA Eckley, MA Nunes Statistics and computing 27, 1129-1143, 2017 | 7 | 2017 |
| Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series RE Wilson, IA Eckley, MA Nunes, T Park Data Mining and Knowledge Discovery 33, 748-772, 2019 | 6 | 2019 |
| Complex-valued wavelet lifting and applications J Hamilton, MA Nunes, MI Knight, P Fryzlewicz Technometrics 60 (1), 48-60, 2018 | 6 | 2018 |
| Modelling and prediction of time series arising on a graph MA Nunes, MI Knight, GP Nason Modeling and Stochastic Learning for Forecasting in High Dimensions, 183-192, 2015 | 6 | 2015 |
