Sentence examples for missing observations are from inspiring English sources

Furthermore, in longitudinal panel data, missing observations are a growing problem also with nonparametric methods when cumulative outcome measures are used.

The general perception is that, to achieve a fixed significance level and power, a group sequential test will require a larger maximum total sample size than required by the corresponding standard single-stage test because missing observations are possible under the group sequential test setting.

The agreement for single measures improves, as expected, when missing observations are replaced.

This approach may be interpreted as a multiple imputation procedure where many sets of missing observations are generated from their a-posteriori distribution.

However, there are limitations; although data from individuals with incomplete follow-up contribute to the model, missing observations are assumed to be random, an assumption implausible in the context of dementia where differential dropout and mortality operate.

Further investigation of this issue is certainly warranted, but the statistical analysis is complicated due to the unbalanced data whereby individuals contribute varying numbers of observations, while data recording standards are changing over time, raising the possibility that missing observations are not random.

For the LVCF method, missing observations were replaced with the last observed value carried forward for reasons other than death or zero value carried forward at death.

In several current large-scale assessment programs (Allen et al. [2001]; Foy et al. [2008]; OECD [2009]) missing observations were dummy coded and the dummy variables were subsequently used in the population model.

In both cases, missing observations were excluded from the model.

Finally, missing observations were imputed based on proximate years using a custom multiple-imputation algorithm [ 14].

To correct for these losses, missing observations were replaced with mean values.