Sentences with phrase «on autocorrelation»

The Student's t test was applied to the bins pairwise as described in the text, adjusting downward the degrees of freedom based on the autocorrelation in the time series.

Ritson at realclimate did not thank me for helpful discussions on autocorrelation despite lengthy correspondence on my part with him.

(Note that empirical AR1 coefficients place less structure on the autocorrelation than the hosking.sim simulations used with the NOAMER tree ring network in our 2005 simulations and simplify this aspect of this analysis.)

Finally, I note that statistical uncertainty has been estimated according to the IPCC AR5 method, which was in turn based on the autocorrelation adjustment method in Santer et al (2008) on which you (Gavin) were a co-author.

momentum is a form of trend following based on autocorrelation.

Not exact matches

All trends were evaluated using Mann — Kendall trend tests, following a four - step process to reduce the effects of serial autocorrelation on significance tests66 (see Supplemental Methods).

The influence of autocorrelation on the ability to detect trend in hydrological series.

The computer scientist Neil Dodgson investigated whether Bridget Riley's stripe paintings could be characterised mathematically, concluding that while separation distance could «provide some characterisation» and global entropy worked on some paintings, autocorrelation failed as Riley's patterns were irregular.

First - differencing, on the other hand, introduces very strong lag - 1 autocorrelation, which I seriously doubt you accounted for.

Arguably the simplest is to just to compute the (lower) effective sample size based on the actual size and the estimated autocorrelation, and use that value to determine your probability (p).

A simple sign test could determine if this is true and, since autocorrelations (both spatial and temporal) should act similarly on records at both extremes, I suspect it could be argued that statistical problems would cancel each other out once the test was adjusted for the correct degrees of freedom.

A 1999 article The Autocorrelation Function and Human Influences on Climate by Tsonis and Elsner commented on Wigley's attempt to prove a human influence was not due to aAutocorrelation Function and Human Influences on Climate by Tsonis and Elsner commented on Wigley's attempt to prove a human influence was not due to autocorrelationautocorrelation.

Throw in the discussion of M - K with a link on the effects of autocorrelation that I can understand and all in all it has been a productive experience for me.

Title: The influence of autocorrelation on the ability to detect trend in hydrological series Authors: Yue, Sheng; Pilon, Paul; Phinney, Bob; Cavadias, George Publication: Hydrological Processes, vol.

The method presented in MM05a generates apparently realistic pseudo tree ring series with autocorrelation (AC) structures like those of the original MBH proxy data (focusing on the 1400 - onward set of proxy tree ring data), using red noise series generated by employing the original proxies» complete AC structure.

The observed autocorrelation (+0.17 based on detrended data) lies just outside the high end of this range, but not significantly so.

Finally, we multiply the interannual regression maps by 1.27, the appropriate value for the 95 % margin of error on a 30 - year trend given a 1 - year lag autocorrelation of the PC timeseries of 0.01.

For a Gaussian time series, the margin of error on a trend of length N t estimated by linear least - squares regression is a function of the magnitude of the interannual variability (given by the standard deviation σ), the lag - one autocorrelation and the trend length (Thompson et al. 2015).

For this calculation, we scale the observed interannual SLP, SAT and P regression values by the factor 1.53 appropriate for a 30 - year trend and an observed NAO autocorrelation of 0.17 based on detrended data during 1920 — 2012 (Fig.

I'm not clear on how you have applied autocorrelation derived from monthly data to annual data.

A subsequent paper, Modeling persistence in hydrological time series using fractional differencing (Water Resources, 1984), outlined a method to derive a particular ARFIMA model from the full autocorrelation function of a time series, and generate a corresponding random synthetic series based on the ARFIMA parameters derived from that autocorrelation structure.

If short term autocorrelations are an issue then they could be addressed by a 12 month moving window instead of arbitrarily partitioning the data based on a human calender that has no physical meaning.

That is what I would base my computation of the DW statistic on and I would strongly suspect that it will show no significant autocorrelations.

Our calculations of the statistical significance of least - squares linear trends in timeseries are based on the two - sided t - test methodology and adjustment for autocorrelation reviewed and outlined by Santer et al. (2000).

I do not understand why there was no discussion of autocorrelation, at least up to and including the OLS regression of one cumulative variable on another cumulative variable.

These results are why I question the use of monthly data with its autocorrelations (that have to be corrected with methods such as Cochrane - Orcutt) when the annual data does not require corrections (that could be of uncertain validity — see Steve M remarks on C - O CIs versus those CIs derived using maximum liklihood approach).

I have seen corrections using a reduction in the number of degrees of freedom in calculating confidence intervals due to autocorrelations of data, but I do not have a finger on how or when to make the correction.

If you have Mandelbrot - type autocorrelation, why are [time] averages or 2nd moments of «climatology» not themselves as transitory as the global average temperature or variance from 3 to 4 pm EST on April 11, 1956?

Second, it seems that David's calculations of the standard deviation are indirectly based on the lag one autocorrelation (he calculated temperature changes for each July to July).

Assuming no autocorrelation for RSS (based on the DW test with GISS data), I regressed the annual temperature anomalies for the period 1979 - 2007 and determined the prediction intervals for individual years shown in the graph below.

I have not done the monthly lag correlations for RSS, but when I did it for the GISS data 1979 - 2007, the DW statistic on the regression residuals showed a very significant positive autocorrelation.

However, we find the estimation of statistical significance ascribed to these results to be in error: MS00 based this calculation on 12 - month smoothed data, from a calculation of the effective sample size (taking into account autocorrelation effects).

Yeah, I'd prefer more samples but I think in this case the autocorrelation can help us get the edges right if we wanted to plot credible intervals (I'd smooth the edges with the same filter I used on the data).

McKitrick just published a sophisticated paper (not OLS, accounting for autocorrelations) saying the no significant warming periods are 16, 19, or 26 years depending on data set analyzed.

For Law Dome d18O over 1931 - 1990 for the central gridcell at lag zero i.e. without any Gergian data mining or data torture, using the HadCRUT3v version on archive, I obtained a detrended correlation of 0.529, with a t - statistic of 4.71 3.65 (for 37 degrees of freedom after allowing for autocorrelation using the prescribed technique)[updated Sep 10, 2016].

Whether a trend is statistically significant does not depend only on the number of years, it depends also on the frequency of data collection, the amount of noise in the system (and the noise in the data collection system), the size of the trend (a smaller trend will take longer to achieve significance), and the amount of autocorrelation in the data series.

Honestly, the p - values should be generated by constructing a Monte Carlo ensemble of model results, per model, and looking at the actual distribution of (and variance of, autocorrelation of, etc) the ensemble of outcomes where the outcomes ARE iid samples drawn from a distribution of model results, and then use a correctly generated mean / sd to determinea p - value on the null hypothesis.

It isn't obvious to me that you need to work on blocks of years if you deal with autocorrelation in the temp series.

Willis wrote: «Steve, you might be interested in the method of Koutsoyiannis for accounting for autocorrelation, which I independently discovered and reported on [at WUWT, 7/1/15, https://wattsupwiththat.com/2015/07/01/a-way-to-calculate-effective-n/]»

Steve, you might be interested in the method of Koutsoyiannis for accounting for autocorrelation, which I independently discovered and reported on here.

Please note that when I subject an OLS regression of dT on ln CO2 for 1880 - 2008, and then perform Cochrane - Orcutt iteration on it to compensate for autocorrelation in the residuals, I still wind up with 60 % of variance accounted for when rho has dropped to an insignificant level.

This is due to the impact of autocorrelation on trend analysis (which was one of the subjects of my post).

The other term is the variance of the estimation error in the regression parameters, and this varies in magnitude depending on the values of the proxies and also the degree of autocorrelation in the errors.

And, in fact, McCulloch did not ask Nature for acknowledment for advising Steig et al of an error (i.e. the failure to take into account the effect of autocorrelation on the significance of estimated trends).

-LSB-...] In the end, the autocorrelation issue turned out to be the least of the original paper's problems: Ryan O'Donnell, Micholas Lewis, Steve McIntyre and Jeff Condon (J. Climate April 2011, 24:2099 - 2115) have shown that the main results of the paper are dependent on oversmoothing that results from retaining too few principal components of the satellite covariance matrix.

Using a sixty - three year average on your data puts the autocorrelation through the roof, making your results statistically insignificant.

The issue that is being discussed here (if I have it right) is the observation that temperature and CO2 concentration time series observations seem to exhibit autocorrelation, that is what you get today is dependent to some extent on what happened in the past.