Sentence examples for missing data solutions from inspiring English sources

We therefore encourage readers to simply view our evaluative work as a template for methodological evaluation of potential missing data solutions in other datasets.

Data access challenges aside, however, our data merging method has numerous advantages over other missing data solutions such as 'imputing to zero'(where a missing value is assumed to mean that the condition is absent), complete case analysis, or multiple imputation.

Other databases and survey questionnaires may have characteristics that would yield different findings and conclusions regarding the optimal missing data solution.

It is such consideration of advantages and disadvantages that has led the APPROACH network to now routinely use the data merging method described here as its missing data solution of choice [ 12].

With a suitable missing data detector, the solutions described in this article open pathways to also efficiently cover reverberated speech and exploit multiple microphones to implement directional hearing.

Note that traditional method based on rough sets will likely to fail when there is severe missing data, and its solution on tolerance relation and tolerance class is more complex, which is not suitable for the large scale and the time varying energy big data.

Although there are various strategies to handle missing data, the best solution is prevention, including minimising patient burden with brief measures.

Mixed-effect models address missing data by providing numerical solutions based on all available data [ 44].

We use the EM algorithm, a classic statistical technique for dealing with missing data, to find maximum-likelihood solutions, which include the conventional least-squares solution as a special case.

The proposed algorithm is also capable of finding accurate solutions with missing data and/or typing errors.

However, the only solution to missing data is not to have any.