Random-effects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the random-effects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of random-effects pattern-mixture models to further handle and describe the influence of missing data in longitudinal studies....

In the last couple of decades, there has been major advancements in the domain of missing data imputation. The techniques in the domain include amongst others: Expectation Maximization, Neural Networks with Evolutionary Algorithms or optimization techniques and K-Nearest Neighbor approaches to solve the problem. The presence of missing data entries in databases render the tasks of decision-maki...

Support vector machine classification (SVM) is a statistical learning method which easily accommodates large numbers of predictors and can discover both linear and non-linear relationships between the predictors and outcomes. A common challenge is constructing an SVM when the training set includes observations with missing predictor values. In this paper, we identify when missing data can bias ...

The goal is to investigate the prediction performance of tree-based techniques when the available training data contains features with missing values. Also the future test cases may contain missing values and thus the methods should be able to generate predictions for such test cases. The missing values are handled either by using surrogate decisions within the trees or by the combination of an...

This paper proves that there is, in every direction in Euclidean space, a line that misses every computably random point. Our proof of this fact shows that a famous set constructed by Besicovitch in 1964 has computable measure 0.

Objectives: Data collection and distribution are essential components required for the victory of Internet Medical Things (IoMT) system. Generally, missing data is most recurrent problem that impacts an overall system performance. Methods: Missing in IoMT systems can be caused by various factors, including faulty connections, external attacks, or sensing errors. Although ubiquitous IoT, imputat...