96 research outputs found

    pinktoe: Semi-automatic Traversal of Trees

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    Tree based methods in S or R are extremely useful and popular. For simple trees and memorable variables it is easy to predict the outcome for a new case using only a standard decision tree diagram. However, for large trees or trees where the variable description is complex the decision tree diagram is often not enough. This article describes pinktoe: an R package containing two tools to assist with the semiautomatic traversal of trees. The PT tool creates a widget for each node to be visited in the tree that is needed to make a decision and permits the user to make decisions using radiobuttons. The pinktoe function generates a suite of HTML and Perl files that permit a CGI-enabled website to issue step-by-step questions to a user wishing to make a prediction using a tree

    LS2W: Implementing the Locally Stationary 2D Wavelet Process Approach in R

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    Locally stationary process representations have recently been proposed and applied to both time series and image analysis applications. This article describes an implementation of the locally stationary two-dimensional wavelet process approach in R. This package permits construction of estimates of spatially localized spectra and localized autocovariance which can be used to characterize structure within images.

    Continuous Time Locally Stationary Wavelet Processes

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    This article introduces the class of continuous time locally stationary wavelet processes. Continuous time models enable us to properly provide scale-based time series models for irregularly-spaced observations for the first time. We derive results for both the theoretical setting, where we assume access to the entire process sample path, and a more practical one, which develops methods for estimating the quantities of interest from sampled time series. The latter estimates are accurately computable in reasonable time by solving the relevant linear integral equation using the iterative thresholding method due to Daubechies, Defrise and De~Mol. We exemplify our new methods by computing spectral and autocovariance estimates on irregularly-spaced heart-rate data obtained from a recent sleep-state study.Comment: 33 pages, 12 figure

    A test for the absence of aliasing or white noise in locally stationary wavelet time series

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    Aliasing is often overlooked in time series analysis but can seriously distort the spectrum, 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 it 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 to model misspecification in that it is unnecessary for the analysis and synthesis wavelets to be identical. Hence, in principle, the tests work irrespective of which wavelet is used to analyze the time series, though in practice there is a tradeoff between increasing statistical power and time localization of the test

    Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic

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    Accurate forecasting of the U.K. gross value added (GVA) is fundamental for measuring the growth of the U.K. economy. A common nonstationarity in GVA data, such as the ABML series, is its increase in variance over time due to inflation. Transformed or inflation-adjusted series can still be challenging for classical stationarity-assuming forecasters. We adopt a different approach that works directly with the GVA series by advancing recent forecasting methods for locally stationary time series. Our approach results in more accurate and reliable forecasts, and continues to work well even when the ABML series becomes highly variable during the COVID pandemic.Comment: 21 pages, 4 figure

    T-tubule disease:Relationship between t-tubule organization and regional contractile performance in human dilated cardiomyopathy

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    Evidence from animal models suggest that t-tubule changes may play an important role in the contractile deficit associated with heart failure. However samples are usually taken at random with no regard as to regional variability present in failing hearts which leads to uncertainty in the relationship between contractile performance and possible t-tubule derangement. Regional contraction in human hearts was measured by tagged cine MRI and model fitting. At transplant, failing hearts were biopsy sampled in identified regions and immunocytochemistry was used to label t-tubules and sarcomeric z-lines. Computer image analysis was used to assess 5 different unbiased measures of t-tubule structure/organization. In regions of failing hearts that showed good contractile performance, t-tubule organization was similar to that seen in normal hearts, with worsening structure correlating with the loss of regional contractile performance. Statistical analysis showed that t-tubule direction was most highly correlated with local contractile performance, followed by the amplitude of the sarcomeric peak in the Fourier transform of the t-tubule image. Other area based measures were less well correlated. We conclude that regional contractile performance in failing human hearts is strongly correlated with the local t-tubule organization. Cluster tree analysis with a functional definition of failing contraction strength allowed a pathological definition of ‘t-tubule disease’. The regional variability in contractile performance and cellular structure is a confounding issue for analysis of samples taken from failing human hearts, although this may be overcome with regional analysis by using tagged cMRI and biopsy mapping