79 results found
Mishra S, Mindermann S, Sharma M, et al., 2021, Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England, ECLINICALMEDICINE, Vol: 39
Nason G, 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), Journal of the Royal Statistical Society Series A: Statistics in Society, ISSN: 0964-1998
Knowledge of the current state of economies, how they respond to COVID-19 mitigations and indicators, and what the future might hold for them is important. We use recently-developed generalised network autoregressive (GNAR) models, using trade-determined networks, to model and forecast the Purchasing Managers’ Indices for a number of countries. We use networks that link countries where the links themselves, or their weights, are determined by the degree of export trade between the countries. We extend these models to include node-specific time series exogenous variables (GNARX models), using this to incorporate COVID-19 mitigation stringency indices and COVID-19death rates into our analysis. The highly parsimonious GNAR models considerably out-perform vector autoregressive models in terms of mean-squared forecasting error and our GNARX models themselves outperform GNAR ones. Further mixed frequency modelling predicts the extent to which that the UK economy will be affected by harsher, weaker or no interventions.
Mishra S, Mindermann S, Sharma M, et al., 2021, Report 44: Recent trends in SARS-CoV-2 variants of concern in England, Report 44: Recent trends in SARS-CoV-2 variants of concern in England, Publisher: Imperial College London, 44
Since its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when, whether, and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital. This is particularly true for England, which has high coverage from vaccines that are likely more protective against B.1.1.7 than some other VOCs. We examine trends in B.1.1.7’s prevalence in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. Our results suggest shifts in the composition of SARS-CoV-2 lineages driving transmission in England between March and April 2021. Local transmission of non-B.1.1.7 VOCs may be increasing; this warrants urgent further investigation.
Nason G, 2020, COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis., Scientific Reports, Vol: 10, Pages: 1-12, ISSN: 2045-2322
Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. At the time of writing, COVID-19 time series for many countries are short: pre- and post-lockdown series are shorter still. Accurate estimation of potentially interesting cycles seems beyond reach with such short series. We solve the problem of obtaining accurate estimates from short series by using recent Bayesian spectral fusion methods. Weshow that transformed daily COVID-19 cases for many countries generally contain three cycles operating at wavelengths of around 2.7, 4.1 and 6.7 days (weekly) and that shorter wavelength cycles are suppressed after lockdown. The pre- and post-lockdown differences suggest that the weekly effect is at least partly due to non-epidemic factors. Unconstrained, new cases grow exponentially, but the internal cyclic structure causes periodic declines. This suggests that lockdown success might only be indicated by four or more daily falls. Spectral learning for epidemic time series contributes to the understanding of the epidemic process and can help evaluate interventions. Spectral fusion is a general technique that can fuse spectra recorded at different sampling rates, which can be applied to a wide range of time series from many disciplines.
Knight M, Leeming K, Nason G, et al., 2020, Generalized Network Autoregressive Processes and the GNAR Package, JOURNAL OF STATISTICAL SOFTWARE, Vol: 96, Pages: 1-36, ISSN: 1548-7660
Chen F, Nason G, 2020, A new method for computing the projection median, its influence curve andtechniques for the production of projected quantile plots, PLoS One, Vol: 15, ISSN: 1932-6203
This article introduces a new formulation of, and method of computation for, theprojection median. Additionally, we explore its behaviour on a specific bivariate set up,providing the first theoretical result on form of the influence curve for the projectionmedian, accompanied by numerical simulations.Via new simulations we comprehensively compare our performance with anestablished method for computing the projection median, as well as other existingmultivariate medians. We focus on answering questions about accuracy andcomputational speed, whilst taking into account the underlying dimensionality. Suchconsiderations are vitally important in situations where the data set is large, or wherethe operations have to be repeated many times and some well-known techniques areextremely computationally expensive.We briefly describe our associated R package that includes our new methods andnovel functionality to produce animated multidimensional projection quantile plots, andalso exhibit its use on some high-dimensional data examples.
Nason G, 2020, Software for "Rapidly evaluating lockdown strategies using spectral analysis"
This is the R software code that produces the analyses and figures in the article "Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown" by Guy Nason
Killick R, Knight MI, Nason GP, et al., 2020, The local partial autocorrelation function and some applications, Publisher: arXiv
The classical regular and partial autocorrelation functions are powerfultools for stationary time series modelling and analysis. However, it isincreasingly recognized that many time series are not stationary and the use ofclassical global autocorrelations can give misleading answers. This articleintroduces two estimators of the local partial autocorrelation function andestablishes their asymptotic properties. The article then illustrates the useof these new estimators on both simulated and real time series. The examplesclearly demonstrate the strong practical benefits of local estimators for timeseries that exhibit nonstationarities.
Eckley IA, Nason GP, 2018, A test for the absence of aliasing or local white noise in locally stationary wavelet time series, Biometrika, Vol: 105, Pages: 833-848, ISSN: 0006-3444
Aliasing is often overlooked in time series analysis but can seriously distort the spectrum, the autocovariance and their estimates. We show that dyadic subsampling of a locally stationary wavelet process, which can cause aliasing, results in a process that is the sum of asymptotic white noise and another locally stationary wavelet process with a modified spectrum. We develop a test for the absence of aliasing in a locally stationary wavelet series at a fixed location, and illustrate its application on simulated data and a wind energy time series. A useful by-product is a new test for local white noise. The tests are robust with respect to model misspecification in that the analysis and synthesis wavelets do not need to be identical. Hence, in principle, the tests work irrespective of which wavelet is used to analyse the time series, although in practice there is a trade-off between increasing statistical power and time localization of the test.
Nason G, 2018, Editorial: Statistical flaws in the teaching excellence and student outcomes framework in UK higher education, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 181, Pages: 923-925, ISSN: 0964-1998
Powell B, Nason G, Elliott D, et al., 2018, Tracking and modelling prices using web-scraped price microdata: towards automated daily consumer price index forecasting, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 181, Pages: 737-756, ISSN: 0964-1998
Cardinali A, Nason GP, 2018, Practical powerful wavelet packet tests for second-order stationarity, APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, Vol: 44, Pages: 558-583, ISSN: 1063-5203
Knight MI, Nason GP, Nunes MA, 2017, A wavelet lifting approach to long-memory estimation, STATISTICS AND COMPUTING, Vol: 27, Pages: 1453-1471, ISSN: 0960-3174
Powell B, Nason GP, Angelini GD, et al., 2017, Optimal Sampling Frequency of Serum Cortisol Concentrations After Cardiac Surgery, CRITICAL CARE MEDICINE, Vol: 45, Pages: E1103-E1104, ISSN: 0090-3493
Cardinali A, Nason GP, 2017, LOCALLY STATIONARY WAVELET PACKET PROCESSES: BASIS SELECTION AND MODEL FITTING, Publisher: WILEY, Pages: 151-174, ISSN: 0143-9782
Nason GP, Powell B, Elliott D, et al., 2017, Should we sample a time series more frequently?: decision support via multirate spectrum estimation, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol: 180, Pages: 353-407, ISSN: 0964-1998
Suppose that we have a historical time series with samples taken at a slow rate, e.g. quarterly. The paper proposes a new method to answer the question: is it worth sampling the series at a faster rate, e.g. monthly? Our contention is that classical time series methods are designed to analyse a series at a single and given sampling rate with the consequence that analysts are not often encouraged to think carefully about what an appropriate sampling rate might be. To answer the sampling rate question we propose a novel Bayesian method that incorporates the historical series, cost information and small amounts of pilot data sampled at the faster rate. The heart of our method is a new Bayesian spectral estimation technique that is capable of coherently using data sampled at multiple rates and is demonstrated to have superior practical performance compared with alternatives. Additionally, we introduce a method for hindcasting historical data at the faster rate. A freeware R package, regspec, is available that implements our methods. We illustrate our work by using official statistics time series including the UK consumer price index and counts of UK residents travelling abroad, but our methods are general and apply to any situation where time series data are collected.
Smith PA, Self A, Michaelson J, et al., 2017, Discussion on the paper by Allin and Hand, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 180, Pages: 24-43, ISSN: 0964-1998
Michis AA, Nason GP, 2017, Case study: shipping trend estimation and prediction via multiscale variance stabilisation, JOURNAL OF APPLIED STATISTICS, Vol: 44, Pages: 2672-2684, ISSN: 0266-4763
Das S, Nason GP, 2016, Measuring the degree of non-stationarity of a time series, STAT, Vol: 5, Pages: 295-305, ISSN: 2049-1573
Nason G, Stevens K, 2015, Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram, PLOS ONE, Vol: 10, ISSN: 1932-6203
Crossman DJ, Young AA, Ruygrok PN, et al., 2015, t-tubule disease: Relationship between t-tubule organization and regional contractile performance in human dilated cardiomyopathy, JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, Vol: 84, Pages: 170-178, ISSN: 0022-2828
Remenyi N, Nicolis O, Nason G, et al., 2014, Image Denoising With 2D Scale-Mixing Complex Wavelet Transforms, IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol: 23, Pages: 5165-5174, ISSN: 1057-7149
Nason GP, 2014, Multiscale variance stabilization via maximum likelihood, BIOMETRIKA, Vol: 101, Pages: 499-504, ISSN: 0006-3444
Nason GP, Savchev D, 2014, White noise testing using wavelets, Stat, Vol: 3, Pages: 351-362
Testing whether a time series is consistent with white noise is an important task within time series analysis and for model fitting and criticism via residual diagnostics. We introduce three fast and efficient white noise tests that assess spectral constancy via the wavelet coefficients of a periodogram. The Haar wavelet white noise test derives the exact distribution of the Haar wavelet coefficients of the asymptotic periodogram under mild conditions. The single-coefficient white noise test uses a single Haar wavelet coefficient obtaining a test statistic as a linear combination of odd-indexed autocorrelations. The general wavelet white noise test uses compactly supported Daubechies wavelets, shows that its coefficients are asymptotically normal and derives its theoretical power for an arbitrary spectrum. All our tests are available in the freely available hwwntest package for the R system. We present a comprehensive simulation study that shows the good performance of our new tests against alternatives commonly found in available software and show an example applied to a wind power time series.
Eckley IA, Nason GP, 2014, Spectral correction for locally stationary Shannon wavelet processes, ELECTRONIC JOURNAL OF STATISTICS, Vol: 8, Pages: 184-200, ISSN: 1935-7524
Nason G, 2013, A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol: 75, Pages: 879-904, ISSN: 1369-7412
Cardinali A, Nason G, 2013, Costationarity of Locally Stationary Time Series Using costat, JOURNAL OF STATISTICAL SOFTWARE, Vol: 55, Pages: 1-22, ISSN: 1548-7660
Knight MI, Nunes MA, Nason GP, 2012, Spectral estimation for locally stationary time series with missing observations, STATISTICS AND COMPUTING, Vol: 22, Pages: 877-895, ISSN: 0960-3174
Eckley IA, Nason GP, 2011, LS2W: Implementing the Locally Stationary 2D Wavelet Process Approach in R, JOURNAL OF STATISTICAL SOFTWARE, Vol: 43, Pages: 1-23, ISSN: 1548-7660
Eckley IA, Nason GP, Treloar RL, 2010, Locally stationary wavelet fields with application to the modelling and analysis of image texture, Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol: 59, Pages: 595-616, ISSN: 0035-9254
Summary: The paper proposes the modelling and analysis of image texture by using an extension of a locally stationary wavelet process model into two dimensions for lattice processes. Such a model permits construction of estimates of a spatially localized spectrum and localized autocovariance which can be used to characterize texture in a multiscale and spatially adaptive way. We provide the necessary theoretical support to show that our two-dimensional extension is properly defined and has the proper statistical convergence properties. Our use of a statistical model permits us to identify, and correct for, a bias in established texture measures based on non-decimated wavelet techniques. The method proposed performs nearly as well as optimal Fourier techniques on stationary textures and outperforms them in non-stationary situations. We illustrate our techniques by using pilled fabric data from a fabric care experiment and simulated tile data. © 2010 Royal Statistical Society.
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