In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...

Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are ...

Feed-forward neural networks (FFNNs) have received considerable attention due to their successful use in a wide variety of statistical applications, including regression and classification problems. A ...

Targeting the Hepatocyte Growth Factor–cMET Axis in Cancer Therapy In most analyses appearing in the medical literature, the most common way of dealing with missing (covariate or response) data is to ...

Data is almost always incomplete. Patients drop out of clinical trials and survey respondents skip questions; schools fail to report scores, and governments ignore elements of their economies. When ...

There are data about practically everything these days, and they can be used to try to answer any number of questions. Do clinical trials really show a drug works? Can surveys really signal who’s ...