Missing Data Dataset

Context:
- It can range from being Missing Completely at Random, to being Missing at Random, to be Missing Not at Random.
Example(s):
- a Censored Dataset.
- …
Counter-Example(s):
- a Noisy Dataset.
See: Anomalous Data, Observed Event.

References

(Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/missing_data Retrieved:2018-3-5.
- 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.
  Missing data can occur because of nonresponse: no information is provided for one or more items or for a whole unit ("subject"). Some items are more likely to generate a nonresponse than others: for example items about private subjects such as income. Attrition ("Dropout") is a type of missingness that can occur in longitudinal studies - for instance studying development where a measurement is repeated after a certain period of time. Missingness occurs when participants drop out before the test ends and one or more measurements are missing.
  Data often are missing in research in economics, sociology, and political science because governments choose not to, or fail to, report critical statistics. Sometimes missing values are caused by the researcher — for example, when data collection is done improperly or mistakes are made in data entry.
  These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing completely at random, missing at random, and missing not at random. Missing data can be handled similarly as censored data.

(Schafer & Graham, 2002) ⇒ Joseph L. Schafer, and John. W. Graham. (2002). “Missing Data: Our view of the state of the art.” In: Psychological Methods, 7(2). [doi>10.1037/1082-989X.7.2.147]

(Rubin, 1976) ⇒ D. B. Rubin. (1976). “Inference and Missing Data." Biometrika, 63.