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For the MB-SW model the error is consequently (29d).
In the ordered logit model the error term is assumed to be logistically distributed, whereas in the ordered probit model the error term is normally distributed.
Moreover, in an error correction model, the error correction term is significant.
In Table 2, we observe that for the benchmark model the error is much larger as for the non-holidays.
Then, in Section 3 we model the error caused by the symbol level limiter in the transmitted signal.
In our model, the error masking/detection effects in instructions based on the Instruction Set Architecture (ISA) are studied.
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For the water model, the errors are smaller than 0.04 for all parameters and station configurations.
We model the errors of ϑ ̂ b - 1 as zero-mean Gaussian with error covariance matrix Pb-1.
In this model, the errors are assumed by a first-order autoregressive process.
For the logistic model, the errors in Equation 2 were assumed to be independent standard logistic random variates.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com