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Linear interpolation is used for the approximation of missing data values.
Thus, we replaced missing data values with the mean for that variable instead of excluding patients with missing data.
However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of raingauge for specific time periods.
An accounting of missing data values should be provided; if imputed data values are used in statistical analyses, the methods used for imputation should be fully described.
We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data.
The Bayesian method avoided the numerical instabilities by using informative prior distributions for the regression coefficients and the missing data values were augmented during the MCMC simulation in a straightforward manner.
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With only one residual drawn from a normal distribution, the imputed missing data value is still treated as unique and fixed.
Missing data value is the absence of the data value completely at random (if missing values of any variable dose not depend on any value) or at random (if missing values in response variable does not depend on in its' own value but dependent on other variables) or may be not at random (if missing values follow some structure or model) [8].
The Markov Chain Monte Carlo method was used with a non-informative prior and a single chain to generate five imputes for each missing data value [ 35, 36].
* Indicates countries with missing data n/a indicates a missing data value * Indicates countries with missing data n/a indicates a missing data value In terms of diet composition, results show that the average DES was 2279 ± 334 kcal/capita/day.
QRISK2-2011 includes a mechanism that replaces missing data, with values from predefined reference values and predictor algorithms.
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