Sentence examples for missing data problem from inspiring English sources

We treated LTFU as a missing data problem, and used multiple imputation [10] to fill in the missing survival times in patients lost to follow-up and hence to obtain estimates of one-year mortality that were adjusted for LTFU.

While the conclusions regarding 'best' solutions to our particular missing data problem are relatively clear, our findings may not be applicable to other missing data scenarios.

In other words, the fundamental problem in any social programme evaluation is the missing data problem.

Howell [8] considered missing data problem for standard experimental studies and observational studies.

This type of missing data problem is referred to as not missing at random or NMAR.

However, the missing data problem, for all of the variables introduced above, inevitably exists for both two datasets.

Despite the fact that NMAR is perhaps the more realistic scenario for missing data problems, advances in handling missing data have generally been made under the assumption of MAR, where the assumption of MCAR is considered mostly unrealistic.

Consequently, Multi-Task Learning (MTL) schemes offer an interesting alternative approach to solve missing data problems.

Missing data problems related to early panel exit and late panel entry are not addressed.

Expectation Maximization (EM) algorithm is another powerful tool to address missing data problems.

Generally speaking, there are two categories of methods for missing data problems: guaranteed cost ones and data imputation based ones.