Peripheralisation: The Missing Link in Dealing with Demographic Change?

Dealing with Missing Data

Rough sets methodology is a useful tool for analysis of decision problems concerning a set of objects described in a data table by a set of condition attributes and by a set of decision attributes. In practical applications, however, the data table is often not complete because some data are missing. To deal with this case, we propose an extension of the rough set methodology to the analysis of...

Psychological Therapies: The Missing Link in Improving Treatment Adherence in Patients with β-thalassemia Major

Dealing with Missing Values in Data

Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Problems associated with missing values are loss of efficiency, complications in handling and analyzing the data and bias resulting from differences between missing and complete data. The important factor for selection ...

Dealing with Missing or Incomplete Data

I n almost any research you perform, there is the potential for missing or incomplete data. Missing data can occur for many reasons: participants can fail to respond to questions (legitimately or illegitimately—more on that later), equipment and data collecting or recording mechanisms can malfunction , subjects can withdraw from studies before they are completed, and data entry errors can occur...

Dealing with Missing Software Project Data

Whilst there is a general consensus that quantitative approaches are an important adjunct to successful software project management there has been relatively little research into many of the obstacles to data collection and analysis in the real world. One feature that characterises many of the data sets we deal with is missing or highly questionable values. Naturally this problem is not unique ...