Sentences with phrase «error variances of»

«The number of buoy observations was multiplied by a factor of 6.8, which was determined by the ratio of random error variances of ship and buoy observations (Reynolds and Smith 1994), suggesting that buoy observations exhibit much lower random variance than ship observations.»

The number of buoy observations was multiplied by a factor of 6.8, which was determined by the ratio of random error variances of ship and buoy observations».

One factor loading on each construct was fixed to one, and the error variance of externalizing symptoms and anxiety symptoms and HbA1C were determined by the formula: Error = VAR (Y) * (1 — reliability)(Hayduk, 1987).

Not exact matches

Ignoring the stratified sampling does not affect point estimates and may have resulted in slightly overestimated standard errors.14 Robust variance estimation was used to allow for the clustered nature of the data within units and trusts.

Additionally, we performed a Hidden Cookie Test to evaluate general olfaction and observed no difference between uninfected and infected animals (Uninfected, Type I -, and Type III - infected animals found the cookie on average within 96 ± 14, 109 ± 18, and 123 ± 31 seconds, respectively where variance indicates Standard Error of the Mean).

This sliding analysis should help overcome problems associated with sampling variance due to the reduced number of rabbits sampled or sequencing errors of individual SNPs.

We have only imperfect measures of teachers» effectiveness and, with one year of data, the variance in the estimation error can be as large as the variance in underlying teacher effects.

Accordingly, and also per the research, this is not getting much better in that, as per the authors of this article as well as many other scholars, (1) «the variance in value - added scores that can be attributed to teacher performance rarely exceeds 10 percent; (2) in many ways «gross» measurement errors that in many ways come, first, from the tests being used to calculate value - added; (3) the restricted ranges in teacher effectiveness scores also given these test scores and their limited stretch, and depth, and instructional insensitivity — this was also at the heart of a recent post whereas in what demonstrated that «the entire range from the 15th percentile of effectiveness to the 85th percentile of [teacher] effectiveness [using the EVAAS] cover [ed] approximately 3.5 raw score points [given the tests used to measure value - added];» (4) context or student, family, school, and community background effects that simply can not be controlled for, or factored out; (5) especially at the classroom / teacher level when students are not randomly assigned to classrooms (and teachers assigned to teach those classrooms)... although this will likely never happen for the sake of improving the sophistication and rigor of the value - added model over students» «best interests.»

We estimate multiple sources of error variance in the setting - level outcome and identify observation procedures to use in the efficacy study that most efficiently reduce these sources of error.

The other 92 % of the variance is due to error and other factors.

Our Gold Cert Coverage provides rescission relief from borrower misrepresentation, underwriting errors and material value variances on qualifying loans after 36 months, regardless of the submission method.

In contrast, the expected return of a passive strategy is 7.5 % (8 % less 0.5 % in costs) with a narrower variance of outcomes that are largely determined by the tracking error of the underlying ETFs.

The total variance in the data gives an upper limit to the errors, and using that upper limit we can compute a statistically reliable estimate of the significance of the trend.

The error bars in state - of - the - art SST compilations take into account such sampling uncertainties, and indeed they become larger back in time, especially the earliest decades (1850s - 1870s) in part due to the fact that there is substantial eddy variance.

I am especially interested in the mathematical details outlined in this sentence; «The total variance in the data gives an upper limit to the errors, and using that upper limit we can compute a statistically reliable estimate of the significance of the trend.»

Mark, by «VERY GOOD» do you mean the reliability, variances and error bars of measuring average global mean temperatures and CO2 mixing ratios over the past 150 years is about as good as measuring your height over the past 30 years?

it seems to me that measurement error on the left side of the graph (the long interval) should have variance in error that reflects the residual error in each grid.

The variance in individual samples is not a result of measurement errors with known Gaussian random noise.

The error term, εij, is distributed as a logistic random variable with set variance of 1.6 [33].

For background behind the flattening - MRES process, glance at estimation theory and take a slightly longer look at least squares (minimizing the sum of the squared errors — related article at Minimum mean squared error — minimizing sum - of - squares and mean squared error are the same thing, and essentially the same thing as minimizing variance and standard deviation).

And if you judge MRES by other criteria than variance or standard deviation, e.g. getting an interesting shape, then you are still within the realm of estimation theory (you're estimating the parameters that give you your interesting shape) but no longer in that of minimum mean squared error.

«CSALT» is a relatively stimulating contribution in the context of generally dull CE commentary, but there are gross errors in its variance partitioning due to total ignorance of geometry.

Tamino had a detailed look at the analysis in that paper, and concluded that there was an error that made it look as though the variance of temperatures was increasing.

Interesting they both report albedo variances of 1 % which is also what Loeb reckons is the level of error on the satellites measurement.

Just for fun my estimates of true - variance contributions over the past one million years: galactic / sun 75 percent, geophysical / ocean 20 percent, atmospheric chemistry / volcano 4 percent, remaining 1 percent (true - variance equals total variance minus natural noise / measurement error).

By the way, although I have not read the paper, «variance corrected means» probably refers to some technique to obtain more accurate estimates of the means, the averages in the series, using information on the variance of the error of estimating these averages which would have varied from year to year.

An error - free laboratory measurement of modern fraction does not imply that the problem collapses into a deterministic look - up from the calibration curve — even if the curve is monotonic over the relevant calendar interval — because the curve itself carries uncertainty in the form of the variance related to the conditional probability of RC age for a given calendar date.

Besides the 6.8 factor is said to be based on the «ratio of random error variances» and nothing else which is why I was looking for that specific term in section 5C.

His 1924 article «On a distribution yielding the error functions of several well known statistics» presented Karl Pearson's chi - squared and Student's t in the same framework as the Gaussian distribution, and his own «analysis of variance» distribution z (more commonly used today in the form of the F distribution).

I fear we are throwing away a lot of variance by starting with monthly Tavg or Tmean and ignoring the variance that comes from the (Tmax - Tmin) / 2 mean std error.

Because of this, any operation that reduces the variance will reduce the RMS error w.r.t. the observations.

The difference between iid and LTP, for example, is confined to the covariance matrices of the corresponding error distribution (the covariance matrix corresponding to iid data is simply an identify matrix multiplied by a scalar variance; for LTP, the off - diagonal elements are non-zero and non-vanishing).

If you don't colour - code the time aspect, you get a spread of data that looks like normal variance or error bars.

Parts of the data may have some elements of the errors that are Gaussian — the example of measurement error in terms of scale may be Gaussian — after get through the problems of variances in the thermometers themselves, which is also a well - known problem for mercury thermometers vis a vis their manufacturing — but their measured variance from the true temperature is not demonstrably Gaussian, and gets worse the further back you go.

Note the implicit swindle in this graph — by forming a mean and standard deviation over model projections and then using the mean as a «most likely» projection and the variance as representative of the range of the error, one is treating the differences between the models as if they are uncorrelated random variates causing > deviation around a true mean!.

Note by the way that MMH is ambiguous on the existence of modelling error: on the one hand they estimate separate, different b coefficients for the different models, on the other, estimate their variances from the temporal variations around those individual model trend lines only.

Most of the cross-sectional non-normality of the errors can be accounting for without bootstrapping by estimating the variance of each series across time as in Loehle and McCulloch 2008.

The grey bar gives an estimate of statistical error, according to a standard formula for error in the estimate of the mean of a time series (in this case the observed time series of Δαs / ΔTs) given the time series» length and variance.

The fraction of variance that is not explained by the proxies is associated with the residuals, and their variance is one part of the mean squared prediction error, which determines the width of the error band.

Adding the relevant years» total uncertainty estimates for the HadCRUT4 21 - year smoothed decadal data (estimated 5 — 95 % ranges 0.17 °C and 0.126 °C), and very generously assuming the variance uncertainty scales inversely with the number of years averaged, gives an error standard deviation for the change in decadal temperature of 0.08 °C (all uncertainty errors are assumed to be normally distributed, and independent except where otherwise stated).

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.

One is the variance of the errors in the regression equation, which is estimated from calibration data, and may be modified in the light of differences between the calibration errors and the validation errors.

«If the experimental errors are uncorrelated, have a mean of zero and a constant variance, the Gauss - Markov theorem states that the least - squares estimator has the minimum variance of all estimators that are linear combinations of the observations.

«This variance increases back in time (the increasingly sparse multiproxy network calibrates smaller fractions of variance), yielding error bars which expand back in time.»

Keep in mind that the limited variance of the first difference errors de facto keeps it bounded over this period.

I2 was 81, indicating that a large proportion of the observed variance in effect sizes may be attributable to heterogeneity rather than to sampling error.

Robust variance (sandwich - type) estimates were used to adjust the standard errors of the parameter estimates for the stratified design effects.

These analyses aim at assessing heritability; that is, estimating how much of the population variance is due to genetic effects (the rest is environmental variance and measurement error).

The «true» score is an abstraction that can never be known for sure, the obtained score is a statistical measurement of the combination of this unknowable score and some error variance.

Following this, we will conduct a meta - analysis of effect sizes and standard errors in RevMan using the generic inverse variance method (Deeks 2011; RevMan 2014).