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These hybrid models caused incorrect 'high' prediction performance and may cause large errors in practice.
But as reported in [28], the distance estimates tend to have positive errors in practice.
This is because the modeling errors in practice, such as the unmodeled friction forces and estimated parameter values.
However, either because models are only an approximation of reality or due to numerical errors, in practice it is impossible to verify whether a solution is exactly periodic.
Until now only ECCs, e.g., BCH codes or LDPC codes, are used to correct errors in practice, since the AWGN sub-channel dominants the performance in the early stage (i.e., few P/E cycles) of flash memories.
Even if it were possible to find the solvability conditions for the mentioned overdetermined problems, since the real data usually have some errors in practice, and thus fall out of the data class for which the existence of a solution is established, it appears that these conditions would not always be satisfactory in applications.
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While the overlap is an error, in practice reporting this as an error can be a nuisance for the user.
Moreover, for the methods of pure additive or multiplicative noise variance estimation, it has been established that the estimation relative error in practice should not be larger than ±20% [8, 18].
Rather, our objective is more straightforward; namely to demonstrate the bias that arises when using self-reported BMI instead of true BMI in labour market analysis and to evaluate a number of alternative approaches that have been suggested for tackling the problem of measurement error in practice.
Three approaches appear most promising in this regard: (1) using 'trigger tools' to identify from electronic health records cases at high risk for diagnostic error; (2) using standardised patients (secret shoppers) to study the rate of error in practice; (3) encouraging both patients and physicians to voluntarily report errors they encounter, and facilitating this process.
In principle it is possible to correct for any arbitrary number of errors, but in practice this may not be possible.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com