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This study investigates whether load estimates and resultant power to detect trends can be enhanced by (i) modelling the error structure so that temporal correlation can be better quantified, (ii) making use of predictive variables, and (iii) by identifying an efficient and feasible sampling strategy that may be used to reduce sampling error.
The weight functions were obtained by modelling the error structure as described by Parresol [83, 85].
A limitation of our approach is the increased complexity of modelling the error variance.
The weight functions were obtained by modelling the error structure as described by Parresol [ 83, 85].
Multivariate Poisson regression will be applied to the number of errors per task with the number of orders per task as the offset (ie, denominator), hence modelling the error rate at task level.
36 GLMs have recently been introduced in cost data analysis and will be the main models, with appropriately specified GLMs modelling the error structure of cost data more closely.
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We model the errors of ϑ ̂ b - 1 as zero-mean Gaussian with error covariance matrix Pb-1.
We described and modelled the errors rate according to the chronology of hospital stay.
The challenge however, is to accurately model the errors in the sequencing data and differentiate real viral mutations, particularly those that exist at low frequencies, from sequencing errors.
We model the channel errors through Bit Error Rate (ber).
In addition, I propose a mixture probability density function of the measurement error to model the "intermittent analytical error".
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com