Sentences with phrase «error variance in»

I should note that if the error variance in one locale is sufficiently close to 0, the algorithm will not change its value by much: the posterior mean will be nearly exactly equal to the data.

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.

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.

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.»

This is because reliability depends on error variance relative to the variance in the value - added estimates.

Ferrari's patent details a setup that eliminates this transmission error and minute variances in steering effort.

In addition to applying advanced statistical techniques, 7 asset managers and index providers often mitigate estimation errors — and address other minimum - variance implementation issues — by imposing constraints on the optimization process.

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 ETFIn 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 ETFin 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.»

Any shifts in variance that is outside the original raises more fundamental concerns about how error and bias are estimated.

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.

In many statistical applications, there is a balance between minimising random sampling error and minimising systematic error (i.e. a trade - off between variance and bias).

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

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.

The most serious error is related to the differing trends or changes in the average temperatures, but an similar error applies also to the variances.

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.

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.

In order to clarify this we would like to discuss a linear relation f (that we suppose to hold for climate sensitivity) first: there, our scheme simply adds the paleo and the fitting error, while IS would add according to Pythagoras (in the Monte Carlo scheme, the variances would add), as the paleo and the fitting error are statistically independenIn order to clarify this we would like to discuss a linear relation f (that we suppose to hold for climate sensitivity) first: there, our scheme simply adds the paleo and the fitting error, while IS would add according to Pythagoras (in the Monte Carlo scheme, the variances would add), as the paleo and the fitting error are statistically independenin the Monte Carlo scheme, the variances would add), as the paleo and the fitting error are statistically independent.

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).

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.

But in the process, the errors, the variance, to be more specific, add at each step.

As to the error bars, consider the reason for the variance in GHG and OA temperature response.

4) If statistical, have the variances been added to the error budget in temperature estimates?

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!.

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.

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.

«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.

Unlike logistic models with only one random error capturing all the variance in the outcome that is unexplained by the model, multilevel models divide the residual variance into three levels, allowing us to capture variation between (i) different parents with the same grandparents; (ii) different grandparent households within the same country, and (iii) different countries.

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).

The variance in ESs across studies with respect to psychopathology could not solely be attributed to sampling error; chronological age appeared to be significantly related to psychopathology in CHD, whereas disease severity was unrelated to psychopathology.