Publications

Refereed Publications

• 1. Nason, G.P. (1992) Entropy in multivariate analysis: projection pursuit. Anal. Proc., 29, 430–435. [PDF] [DOI]
• 2. Nason, G.P. and Sibson, R. (1992) Measuring multimodality. Stat. Comput., 2, 153–160. [DOI]
• 3. Nason, G.P. and Silverman, B.W. (1994) The discrete wavelet transform in S. J. Comput. Graph. Stat., 3, 163–191. [PDF] [JSTOR-DOI] [DOI]
• 4. Nason, G.P. (1995) Three-dimensional projection pursuit. J. R. Stat. Soc. C, 44, 411–430. [PDF] [JSTOR-DOI] [DOI]
• 5. Nason, G.P. (1996) Wavelet shrinkage using cross-validation. J. R. Stat. Soc. B., 58, 463–479. [PDF] [JSTOR] [DOI]
• 6. Hall, P. and Nason, G.P. (1997) On choosing a non-integer resolution level when using wavelet methods. Stat. Prob. Lett., 34, 5–11. [PDF] [PDF Figure 1] [DOI]
• 7. Burn, J.F., Wilson, A.M. and Nason, G.P. (1997) Impact during equine locomotion. Equine Vet. J. supplement, 23, 9–12. [PDF]
• 8. von Sachs, R., Nason, G.P. and Kroisandt, G. (1999) Spectral representation and estimation for locally-stationary wavelet processes. In Spline Functions and the Theory of Wavelets, CRM Proceedings and Lecture Notes, (S. Dubuc and G. Deslauriers, ed), 18, 381–397. Providence, RI: American Mathematical Society. [DOI]
• 9. Nason, G.P. and von Sachs, R. (1999) Wavelets in time series analysis. Philos. T. R. Soc. A, 357, 2511–2526. [PDF] [JSTOR] [DOI]
• 10. Antoniadis, A, Grégoire, G. and Nason, G.P. (1999) Density and hazard rate estimation for right censored data using wavelet methods. J. R. Stat. Soc. B, 61, 63–84. [PDF] [JSTOR] [DOI]
• 11. Morgan, R. and Nason, G.P. (1999) Wavelet shrinkage of itch response data. Revue de Statistique Appliquée, 47, 81–98. [PDF] [JOURNAL]
• 12. Nason, G.P., von Sachs, R. and Kroisandt, G. (2000) Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. J. R. Stat. Soc. B, 62, 271–292. [PDF] [JSTOR] [DOI]
• 13. Hunt, K. and Nason, G.P. (2001) Wind speed modelling and short-term prediction using wavelets. Wind Engineering, 25, 55–61. [PDF] [JSTOR] [DOI]
• 14. Herrick, D.R.M., Nason, G.P. and Silverman, B.W. (2001) Some new methods for wavelet density estimation. Sankhyā, Series A, 63, 394–411 [PDF] [JSTOR]
• 15. Nason, G.P., Sapatinas, T. and Sawczenko, A. (2001) Wavelet packet modelling of infant sleep state using heart rate data. Sankhyā, Series B, 63, 199–217 [PDF] [JSTOR]
• 16. Ascione, R., GPN, Al-Ruzzeh, S., Ko, C., Ciulli, F. and Angelini, G.D. (2001) Coronary revascularization with or without cardiopulmonary bypass in patients with preoperative nondialysis-dependent renal insufficiency. Ann. Thor. Surg., 72, 2020–2025. [DOI]
• 17. Nason, G.P. (2001) Robust projection indices. J. R. Stat. Soc. B, 63, 551–567. [PDF] [JSTOR] [DOI]
• 18. Barber, S., Nason, G.P. and Silverman, B.W. (2002) Posterior probability intervals for wavelet thresholding. J. R. Stat. Soc. B, 64, 189–206. [PDF] [JSTOR] [DOI]
• 19. Nason, G.P. (2002) Choice of wavelet smoothness, primary resolution and threshold in wavelet shrinkage. Stat. Comput., 12, 219–227. [PDF] [DOI]
• 20. Nason, G.P. and Sapatinas, T. (2002) Wavelet packet transfer function modelling of nonstationary time series. Stat. Comput., 12, 45–56. [PDF] [DOI]
• 21. Barber, S. and Nason, G.P. (2004) Real nonparametric regression using complex wavelets. J. R. Stat. Soc. B, 66, 927–939. [PDF] [JSTOR] [DOI]
• 22. Fryzlewicz, P. and Nason, G.P. (2004) A Haar-Fisz algorithm for Poisson Intensity Estimation. J. Comput. Graph. Stat., 13, 621–638. [PDF] [JSTOR] [DOI]
• 23. Diraison, F., Xavier, G.D., Motakis, E., GPN, Rutter, G.A. and Leclerc, I. (2004) Impact of over-expression of carbohydrate response-element binding protein (ChREBP) on pancreatic beta-cell gene expression profile and insulin secretion. Diabetologia, 47, 439 (Supplement 1). [UBris]
• 24. Diraison, F., Motakis, E., Parton, L.E. GPN, Leclerc, I. and Rutter, G.A. (2004) Impact of adenoviral transduction with SREBP1c or AMPK on pancreatic islet gene expression profile – Analysis with oligonucleotide microarrays. Diabetes, 53, S84–S91, (Supplement 3) [DOI]
• 25. Nason, G.P. (2005) pinktoe: semi-automatic traversal of trees. J. Stat. Softw., 14(1), 1–11. [DOI]
• 26. Cardinali, A. and Nason, G.P. (2005) A statistical multiscale approach to image segmentation and fusion. Proceedings of the Seventh International Conference on Information Fusion, 475–482. IEEE [PDF] [DOI]
• 27. Eckley, I.A. and Nason, G.P. (2005) Efficient computation of the discrete autocorrelation wavelet inner product matrix. Stat. Comput., 15, 83–92. [PDF] [DOI]
• 28. Motakis, E.S., Nason, G.P., Fryzlewicz, P. and Rutter, G.A. (2006) Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach. Bioinformatics, 22, 2547–2553. [PDF] [DOI]
• 29. Nason, G.P. (2006) Stationary and non-stationary time series. Chapter 11 of Statistics in Volcanology, (Mader, H. and Coles, S.C., eds), Geological Society of London [DOI]
• 30. Fryzlewicz, P. and Nason, G.P. (2006) Haar-Fisz estimation of evolutionary wavelet spectra. J. R. Stat. Soc. B, 68, 611–634. [PDF] [JSTOR] [DOI]
• 31. Nason, G.P. (2006) On the sum of t and Gaussian random variables. Stat. Prob. Lett., 76, 1280–1286. [PDF] [DOI]
• 32. Nunes, M., Knight, M. and Nason, G.P. (2006) Adaptive lifting for nonparametric regression. Stat. Comput., 16, 143–159. [PDF] [DOI]
• 33. Knight, M. and Nason, G.P. (2006) Improving Prediction of Hydrophobic Segments along a Transmembrane Protein Sequence using Adaptive Multiscale Lifting. SIAM Journal on Multiscale Modeling and Simulation, 5, 115–129. [PDF] [DOI]
• 34. Triantafyllopoulos, K. and Nason, G.P. (2007) A Bayesian analysis of moving average processes with time-dependent parameters. Comput. Stat. Data An., 52, 1025–1046. [DOI]
• 35. Fryzlewicz, P., Delouille, V. and Nason, G.P. (2007) GOES-8 X-ray sensor variance stabilization using the multiscale data-driven Haar-Fisz transform. J. R. Stat. Soc. C, 56, 99–116 [PDF] [JSTOR] [DOI]
• 36. Bailey, D. and Nason, G.P. (2008) Cohesion of Major Political Parties. Brit. Polit., 3, 390–417 [DOI]
• 37. Fryzlewicz, P., Nason, G.P. and von Sachs, R. (2008) A wavelet-Fisz approach to spectrum estimation. J. Time Ser. Anal., 29, 868–880. [PDF]. Longer Technical Report 08:05 version containing full proofs and extras: [PDF] [DOI]
• 38. Nason, G.P. and Bailey, D. (2008) Estimating the intensity of conflict in Iraq. J. R. Stat. Soc. A, 171, 899–91. [JSTOR] [DOI]
• 39. Mahadevan, N., Nason, G.P. and Munro, A. (2008) Multi-dimensional network function estimation. 2008 IEEE International Conference on Communications, 19–23 May 2008. Beijing, 468–472. [DOI]
• 40. Nunes, M. and Nason, G.P. (2009) A multiscale variance stabilization for binomial sequence proportion estimation. Stat. Sinica, 19, 1491–1510. [PDF] [JSTOR] [JOURNAL]
• 41. Jansen, M., Nason, G.P. and Silverman, B.W. (2009) Multiscale methods for data on graphs and irregular multidimensional situations. J. R. Stat. Soc. B, 71, 97–126. See Research Report 08:07 for the tech report version of this paper. [JSTOR] [DOI]
• 42. Knight, M. and Nason, G.P. (2009) A `nondecimated' lifting transform. Stat. Comput., 19, 1–16. [PDF] [DOI]
• 43. Triantafyllopoulos, K. and Nason, G.P. (2009) A note on state-space representations of locally stationary wavelet time series. Stat. Prob. Lett., 79, 50–54. [DOI] [arXiv]
• 44. Cardinali, A. and Nason, G.P. (2010) Costationarity of locally stationary time series. J. Time Series Econometrics, 2, Issue 2, Article 1. [PDF] [DOI]
• 45. Eckley, I.A., Nason, G.P. and Treloar, R.L. (2010) Locally stationary wavelet fields with application to the modelling and analysis of image texture. J. R. Stat. Soc. C, 59, 595–616. [PDF] [JSTOR] [DOI]
• 46. Eckley, I.A. and Nason, G.P. (2011) LS2W: Implementing the Locally Stationary 2-D Wavelet Process Approach in R. J. Stat. Softw., 43(3), 1–23. [DOI]
• 47. Knight, M., Nunes, M. and Nason, G.P. (2012) Spectral estimation for locally stationary time series with missing observations. Stat. Comput., 22, 877–895. [PDF] [DOI]
• 48. Nason, G.P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Stat. Soc. B, 75, 879–904. [PDF] (Note: there is a typo on page 15 in the definition of the P4 model. The constant 4 in the definition of S_1 should be 64.) Erratum: A small correction to Theorem 1 on page 19 has been made. Better tests of stationarity can be found below in publication 56. [JSTOR] [DOI] [Supplementary]
• 49. Cardinali, A. and Nason, G.P. (2013) Costationarity of Locally Stationary Time Series Using costat. J. Stat. Softw., 55(1), 1-22. [DOI]
• 50. Nason, G.P. (2014) Multiscale Variance Stabilization via Maximum Likelihood. Biometrika, 101, 499–504. Tech. Report Version: [PDF] [JSTOR] [DOI]
• 51. Eckley, I.A. and Nason, G.P. (2014) Spectral correction for locally stationary Shannon wavelet processes. Electron. J. Stat., 8, 184–200. [EUCLID]
• 52. Remenyi, N., Nicolis, O., Nason, G.P. and Vidakovic, B. (2014) Image Denoising with 2-D Scale-Mixing Complex Wavelet Transforms. IEEE T. Image Process., 23, 5165–5174. [PDF] [DOI]
• 53. Nason, G.P. and Savchev, D. (2014) White noise testing using wavelets. STAT, 3, 351–362. [DOI]
• 54. Crossman, D.J., Young, A.A., Ruygrok, P.N., Nason, G.P., Baddeley, D., Soeller, C. and Cannell, M.B. (2015) T-tubule disease: Relationship between t-tubule organisation and regional contractile performance in human dilated cardiomyopathy. J. Mol. Cell. Cardiol., 84, 170–178. [DOI]
• 55. Nason, G.P. and Stevens, K. (2015) Bayesian wavelet shrinkage of the Haar-Fisz transformed wavelet periodogram. PLOS ONE, [DOI] [arXiv]
• 56. Knight, M., Nason, G.P. and Nunes, M. (2016) A wavelet approach to long-memory estimation. Stat. Comput., 27, 1453–1471. [DOI]
• 57. Das, S. and Nason, G.P. (2016) Measuring the degree of nonstationarity of a time series. STAT, 5, 295–305. [DOI]
• 58. Michis, A.A. and Nason, G.P. (2017) Case Study: Shipping trend estimation and prediction via multiscale variance stabilisation. J. Appl. Stat., 44, 2672–2684. [DOI]
• 59. Cardinali, A. and Nason, G.P. (2017) Locally stationary wavelet packet processes: basis selection and model fitting. J. Time Ser. Anal., 38, 151–174. [DOI]
• 60. Nason, G.P., Powell, B., Elliott, D. and Smith, P.A. (2017) Should we sample a time series more frequently? Decision support via multirate spectrum estimation (with discussion). J. R. Stat. Soc. A, 180, 353–407. [JSTOR] [DOI] [Supplementary]
• 61. Powell, B., Nason, G.P., Angelini, G.D., Lightman, S.L. and Gibbison, B. (2017) Optimal Sampling Frequency of Serum Cortisol Concentrations After Cardiac Surgery. Crit. Care Med., 45(10), e1103–e1104. [DOI]
• 62. Cardinali, A. and Nason, G.P. (2018) Practical Powerful Wavelet Packet Tests for Second-order Stationarity. Appl. Comput. Harmon. A., 44, 558–583. [PDF] [DOI]
• 63. Powell, B., Nason, G.P., Elliott, D., Mayhew, M., Davies, J. and Winton, J. (2018) Tracking and modelling prices using web-scraped price microdata: toward automated daily CPI forecasting. J. R. Stat. Soc. A, 181, 737–756. [JSTOR] [DOI]
• 64. Eckley, I.A. and Nason, G.P. (2018) A test for the absence of aliasing or local white noise in locally stationary wavelet series. Biometrika, 105, 833–848. [JSTOR] [DOI]
• 65. Nason, G.P. (2018) Statistical Flaws in the Teaching Excellence and Student Outcomes Framework in UK higher education. J. R. Stat. Soc. A, 181, 923–925. [JSTOR] [DOI]
• 66. Knight, M., Leeming, K., Nason, G.P. and Nunes, M. (2020) Generalised Network Autoregressive Processes and the GNAR package. J. Stat. Softw., 96(5), 1–36 [DOI] [arXiv] [Earlier arXiv version]
• 67. Chen, F. and Nason, G.P. (2020) A new method for computing the projection median, its influence curve and techniques for the production of projected quantile plots. PLOS ONE. [DOI]
• 68. Killick, R., Knight, M.I., Nason, G.P. and Eckley, I.A. (2020) The local partial autocorrelation function and some applications. Electron. J. Stat., 14, 3268–3314. [DOI] [Fuller arXiv]
• 69. Nason, G.P. (2020) COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis. Sci. Rep-UK, 10, 22134. [DOI] [arXiv]
• 70. Mishra,S., Mindermann,S., Sharma, M., Whittaker, C., Mellan, T.A., Wilton, T., Klapsa, D., Mate, R., Fritzsche, M., Zambon, M., Ahuja, J., Howes, A., Miscouridou, X., Nason, G.P., Ratmann, O., Semenova, E., Leech, G., Sandkuhler, J., Rogers-Smith, M., Vollmer, M., Unwin J.T., The COVID-19 Genomics UK (COG-UK) Consortium, Gal, Y., Chand, M., Gandy, A., MArtin, J., Volz, E., Ferguson, N.M., Bhatt, S., Brauner, J.M. and Flaxman, S. (2021) Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England. EClinicalMedcine (A Lancet Journal), 39 101064. [DOI]
• 71. Nason, G.P. and Wei, J. (2021) Quantifying the economic response to COVID-19 mitigations and death rates via forecasting Purchasing Managers' Indices using Generalised Network Autoregressive models with exogenous variables (with discussion). J. R. Stat. Soc. A, 185, 1778–1792. [DOI] [Report 45, Imperial COVID-19 Response version] [arXiv] [YouTube]
• 72. Palasciano, H. and Nason, G.P. (2023) A Test for the Absence of Aliasing or White Noise in Two-Dimensional Locally Stationary Wavelet Processes. Stat. Comput., 33, 108. [DOI]
• 73. Killick, R., Knight, M.I., Nason, G.P., Nunes, M. and Eckley, I.A. (2024) Automatic locally stationary time series forecasting with application to predicting UK gross value added time series. J. R. Stat. Soc. C, 74, 18–33. [DOI] [arXiv]
• 74. Palasciano, H., Knight, M. and Nason, G.P. (2025) Continuous Time Locally Stationary Wavelet Processes. Biometrika, 112, asaf015. [DOI] [arXiv]
• 75. Nason, G.P., Salnikov, D. and Cortina-Borja, M. (2025) New tools for network time series with an application to COVID-19 hospitalisations (with discussion), J. R. Stat. Soc. A, 188, (to appear) [arXiv]
• 76. Nason, G.P. and Palasciano, H.A. (2025) Forecasting UK Consumer Price Inflation with RaGNAR: Random Generalised Network Autoregressive Processes. Int. J. Forecasting, 41, (in press) [DOI] [arXiv]
• 77. Wei, J. and Nason, G.P. (2025) Full Nonparametric MIDAS: A new approach for nonparametric mixed frequency time series regression. Electron. J. Stat., 19, 3292–3316. [DOI]
• 78. Cao, D., Knight, M.I. and Nason, G.P. (2025) A multiscale method for data collected from network edges via the line graph. Stat. Comput., 35, (to appear) [arXiv] [DOI(not OpenAccess)] [OpenAccess Accepted Manuscript for Statistics and Computing]