Data Management: Missing Data (Definitions)

"Noise in a bivalent testing input pattern in which one or more components have been changed from the correct value to a value midway between the correct and the incorrect value, i.e. a + 1, or a -1, has been changed to a O." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"Many databases have cases where not all the attribute values are known. These can be due to structural reasons (e.g., parity for males), due to changes or variations in data collection methodology, or due to nonresponses. In the latter case, it is important to distinguish between ignorable and nonignorable nonresponse. The former must be addressed even though the latter can (usually) be treated as random." (William J Raynor Jr., "The International Dictionary of Artificial Intelligence", 1999)

"Observations where one or more variables contain no value." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"data are said to be missing when there is no information for one or more pattern on one or more features in a research study." (Pedro J García-Laencina et al, "Classification with Incomplete Data", 2010)

"Missing data, also known as lost data, is the data that is lost in an inner join when rows of the tables being joined do not match with any other rows. Missing data can also occur with one-sided joins on the side that is not being preserved. This definition ignores all the other reasons for missing data." (Michael M David & Lee Fesperman, "Advanced SQL Dynamic Data Modeling and Hierarchical Processing", 2013)

"It refers that no data value is stored for the variable in the observation." (Liang-Ting Tsai et al, "Weighting Imputation for Categorical Data", 2014)

"Observations which were planned and are missing." (OECD)

"In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data." (Wikipedia)