Nicole Scott is an FBI agent who finds missing people with her new partner, a young psychic.

Missing - Netflix

Type: Scripted

Languages: English

Status: Ended

Runtime: 60 minutes

Premier: 2003-08-02

Missing - Missing data - Netflix

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.

Missing - Model-based techniques - Netflix

Finally, the estimands that emerge from these techniques are derived in closed form and do not require iterative procedures such as Expectation Maximization that are susceptible to local optima. A special class of problems appears when the probability of the missingness depends on time. For example, in the trauma databases the probability to loose data about the trauma outcome depends on the day after trauma. In these cases various non-stationary Markov chain models are applied.

P        (        X                  |                Y        )              {\displaystyle P(X|Y)}   from complete data and multiplying it by                     P        (        Y        )              {\displaystyle P(Y)}   estimated from cases in which Y is observed regardless of the status of X. Moreover, in order to obtain a consistent estimate it is crucial that the first term be                     P        (        X                  |                Y        )              {\displaystyle P(X|Y)}   as opposed to                     P        (        Y                  |                X        )              {\displaystyle P(Y|X)}  .

Different model structures may yield different estimands and different procedures of estimation whenever consistent estimation is possible. The preceding estimand calls for first estimating

Missing - References - Netflix