TAUEST: a computer program for estimating persistence in unevenly spaced weather/climate time series

By Manfred Mudelsee. 2002

97 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

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Citations in the corpus

Trend analysis of climate time series: A review of methods (2019, background): “…as the minimizer of the least-squares sum, = × = S r i t i t i r i( ) [ ( ) exp{ [ ( ) ( 1)]/ } ( 1)] . i n 2 2 (A.3) The idea behind that formula (Mudelsee, 2002) is that the memory is the larger, the smaller the time distance from a time point, t(i), to the preceding time point, t(i−1), is.”
More accurate, calibrated bootstrap confidence intervals for estimating the correlation between two time series (2014, methodology): “The persistence times, τX and τY , are estimated with a least-squares algorithm (Mudelsee 2002).”
Slow dynamics of the Northern Hemisphere glaciation (2005, methodology): “tions by the decay period of the autocorrelation function [ Mudelsee, 2002 ] fitted to the standardized regression residuals (a residual is given by the difference between the d 18 O value and the ramp model value, divided by s(t)).”
Estimating Pearson's Correlation Coefficient with Bootstrap Confidence Interval from Serially Dependent Time Series (2003, methodology): “However, PearsonT performs only one iteration loop: estimation ofτx and τy from their respective time series using the method of Mudelsee (2002).”
REDFIT-X: Cross-spectral analysis of unevenly spaced paleoclimate time series (2016, methodology): “They are estimated with the least-squares algorithm TAUEST (Mudelsee, 2002), which fits a first-order autoregressive or AR(1) persistence model to unevenly spaced time series.”
REDFIT: estimating red-noise spectra directly from unevenly spaced paleoclimatic time series (2002, methodology): “The unknown value of t is estimated from an unevenly spaced time series usingthe least-squares algorithm devised by Mudelsee (2002) .”
Similarity estimators for irregular and age-uncertain time series (2013, methodology): “Apart fromF = S,F may represent any bivariate statistic, and with minor modification is also applicable to calculate the influence of sampling uncertainty on univariate statistics, like the autocorrelation coefficients or persistence times (Rehfeld et al., 2011; Mudelsee, 2002).”