Sentence examples for between the error structures from inspiring English sources

Noticeable similarities between the error structures of the satellite products derived from same retrieval algorithm and same measuring frequency however suggest transferability of error parameters between them.

Here we assume samples are independent; in time-course data, that would not necessarily be the case and the error structure between samples would need to be altered (in Equation 4 and subsequent derivations) to account for the longitudinal nature of such data.

This indicates that the error structure of the model accurately describes residual variability in the data.

The multi-scale error structures are found to be non-trivial and vary between the products, giving cause for conducting multi-scale merging with awareness of these differences.

However, if the relationships between methods, as well as their error structures, were known, this problem could be addressed by use of correction and calibration coefficients, which would allow harmonisation of the results obtained from the different methods.

Considering the auto-correlation structure between the error terms, observations in successive profiles can be expressed by (y_{ij} = A_{0} + A_{1} x_{i} + varepsilon_{ij}) and (y_{{left( {i - 1} right)j}} = A_{0} + A_{1} x_{{left( {i - 1} right)}} + varepsilon_{{left( {i - 1} right)j}}).

Proportional, additive, and combined proportional and additive error structures were evaluated for the residual error.

Exponential, additive, and combined error structures were evaluated for the residual error terms.

Differences in population prevalence between species were tested with a logistic regression model with binomial error structure applying the GENMOD procedure in SAS/enterprise while estimating the scaling parameter by the square root of DEVIANCE/DOF.

The error was modelled using a continuous autoregressive error structure (CAR, which accounted for the time between measurements.

We assumed a binomial error structure and a logit link between the response variables and the linear combination of the explanatory variables.