Sentence examples for missing data problems from inspiring English sources

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.

The two-phase framework can be applied in missing data problems, sampling at multiple occasions, and situations without a good frame.

First focusing on the general finite mixture of multinomials, we show that the AFSA approach is closely related to Expectation Maximization, and can similarly be generalized to other finite mixtures and other missing data problems.

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

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.

Geostatistical approach could help in solving the missing data problem or helping in finding data in case of large meshes applied on the area under test.

It first presents the evaluation problem as a missing data problem and then considers various solutions proposed in the statistics and econometrics literature.

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