Sentences with phrase «of error estimation»

Techniques of Error Estimation.

Roache deals with CFD and with the science of error estimation / propagation that is 100 years old.

Though error estimation was not not done (more accurately, not reported or discussed) in the BEST study, kriging by its nature does a lot of error estimation.

Not exact matches

Of course, better technology may increase the accuracy of predictions but serious errors in estimation of availability of reserves have been known to happeOf course, better technology may increase the accuracy of predictions but serious errors in estimation of availability of reserves have been known to happeof predictions but serious errors in estimation of availability of reserves have been known to happeof availability of reserves have been known to happeof reserves have been known to happen.

Errors of estimation, usually innocent but sometimes not, can be huge.

Calculation to determine the necessary number of resident participants used estimations of knowledge from previous studies of pediatric residents, which suggested a baseline knowledge about breastfeeding of 60 percentage points.8, 9,15 To detect an improvement in knowledge score of 20 percentage points with an estimated standard deviation of 20 points, a 2 - tailed α error of 0.05 and a power of 0.80, sample size was calculated at a minimum of 16 resident participants.

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.

This process made even more difficult, in Adonis's estimation, by the number of people who had basically given up: David Blunkett, John Reid, even Blair, who argued that it was a «serious error» to keep fighting.

Typically the JP is only uniform where the estimation is of a simple location parameter, with the measured variable being the parameter (or a linear function thereof) plus an error whose distribution is independent of the parameter.

DF - analyses of heritability with double - entry twin data: asymptotic standard errors and efficient estimation

On top of that, rarely do these people accurately know their body fat percentages (other than very optimistic guesses) and even when measurements are taken almost all body fat estimation techniques (including skin-folds, BIA, etc) are subject to up to 4 % error.

Attention to test scores in the value - added estimation raises issues of the narrowness of the tests, of the limited numbers of teachers in tested subjects and grades, of the accuracy of linking teachers and students, and of the measurement errors in the achievement tests.

To highlight the role of random error, we calculated the «null distribution,» or what the distribution of program rankings would look like if all the programs were actually identical and nothing but random estimation error were present.

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.

I wouldn't necessarily read too much into the AE - derived October number being 2.4 % lower than the Amazon - derived one for September: it could just as easily be estimation error — for example, as mentioned earlier, our estimated US share of the overall global Kindle pot, at 77.5 %, could have actually been high by 2.5 %... or, for that matter, low by 2.5 %.

Was each estimate of the power of the value effect subject to its own estimation error, overlooked by much of the quant community at their peril?

All volatility, beta, and tracking error expectations are based upon an exponential decay - weighted estimation of recent volatility, beta, and tracking error and are not a guarantee of future volatility, beta, or tracking error.

Model parameters were estimated using a finite amount of data and are therefore subject to estimation error.

This just means that you buy at a big enough discount to allow some room for error in your estimation of value.

Five - Year Forecasts We summarize the valuation ratios, historical returns, historical returns net of valuation changes, and expected returns along with estimation errors for the most popular factors and strategies in Table 2.

The proposed scheme uses the channel estimation matrix for detection and treats the interference caused by channel estimation errors and additive white Gaussian noise as equivalent noise where the channel estimation matrix and the statistical characteristic of channel estimation errors are necessitated.

If you've ever taken a statistics or econometrics course, you might recognize the calculation and application of the left - inverse as the ordinary least squares (OLS) estimation of the weights; that means that in this case, «closest» implies that the weights we found minimize the sum of the squares of the differences («errors» or «residuals») between our replicated portfolio and VTSMX.

Graham's margin of safety, the difference between a stock's price and its intrinsic value, provides you cushion against estimation errors.

How do we know that it was the anthropogenics (commonly referred to as CO2 & the subject of the political Kyoto decision) that resulted in the closer estimation and not some competing / compensating errors in the natural model that do not show up until the 1970 - 2000 etc temp rises?

If one parameter is off, then the error here will propagate to affect the estimation of a neighboring parameter, and so on.

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.

For the estimation of the total ocean heat content (OHC) a lesser precision would probably be almost as good, because errors of individual measurements always cancel to a large extent as long as the floats do not have common systematic errors.

The statistical assessment of errors in model prediction and model estimation is of fundamental importance.

Statistics as such has a part to play in error estimation, assessing observational reliability, and, e.g., in the mathematical expression of Thermodynamics and Statistical Mechanics.

Based on NCEP - NCAR reanalysis data (41), we prescribe κ the value of 0.31 ± 0.05 (see Estimation of the Error Bars for more details regarding estimation of the error baEstimation of the Error Bars for more details regarding estimation of the error bars foError Bars for more details regarding estimation of the error baestimation of the error bars foerror bars for κ).

Refer to Estimation of the Error Bars for a discussion of the error bars on these amplitError Bars for a discussion of the error bars on these ampliterror bars on these amplitudes.

Error estimation is a whole subject on it's own that Judith has given a lot of attention to in the past.

Boltzmann is used in classical nucleation theory — and the fact that there is minimal difference to B - E — especially wrt errors of estimation and measurement of the relevant factors — is obvious but not important.

I believe the current length is about 2,000 years and for the earliest portion it is impossible to resolve fine scale questions — in fact only the most general questions can be informed by the sparse grid of data points available and some regions are white space — no estimate possible — and errors of estimation are large for the available points.

They don't give any error estimation for the 1880 - 2009 «acceleration» of 0.001 mm · yr ^ 2 but it is clearly it is not significant.

It is this lack of a formal, certified approach to error estimation rampant in climate research that has led to a good deal of scepticism.

General Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in Time Series

Repeat the need for proper estimation and reporting of error.

I must add that the error associated with the uncertain estimation of the magnitude and time of application of the TOBS correction is also a systematic, non-random error.

A problem I have with TOBs papers is that the statement of the method is not clearly posed, the integration and interpolation are done simultaneously and hence any sort of standard quadrature error estimation is unavailable.

True in the sense that even though the average temperature reconstruction «makes sense» there is zero formal error estimation of either the interpolation error of the global surface temperature reconstruction, or the quadrature thereof and hence the uncertainty in the temperature record is completely unknown (save maybe sound extreme bounds that one could probably work out on a napkin).

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).

Quantifying errors in the estimation of basin - mean precipitation.

This is a straightforward consequence of the variability in the parameter estimation (not to mention measurement errors in the various observations).

Typically the JP is only uniform where the estimation is of a simple location parameter, with the measured variable being the parameter (or a linear function thereof) plus an error whose distribution is independent of the parameter.

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.

Estimation of the Impact of Sampling Errors in the VOS Observations on Air — Sea Fluxes.

The Estimation of the Error at Richardson's Extrapolation and the Numerical Solution of Integral Equations of the Second Kind

Mplus v7.11 was used for all analyses.23 SDQ items were treated as ordinal, with weighted least - squares means and variance — adjusted estimation used.23 Given the χ2 statistic's propensity to reject good models when samples are large and / or complex, the comparative fit index (CFI) and root mean square error of approximation (RMSEA) were used to assess model fit.