PMD4 WHEN CAN MISSING DATA BE CONSIDERED MISSING AT RANDOM (MAR) IN SUBSTANCE ABUSE TREATMENT OUTCOMES RESEARCH?

A New Approach to Estimation of Response Probabilities when Missing Data are Not missing at Random

Often the probability of responding depends directly on the outcome value. This case can be treated by postulating a parametric model for the distribution of the outcomes before nonresponse and a model for the response mechanism. The two models define a parametric model for the joint distribution of the outcomes and response indicators, and therefore the parameters of these models can be estima...

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

Distinguishing "Missing at Random"i and "Missing Completely at Randomn"

Optimal Design When Outcome Values Are Not Missing at Random

The presence of missing values complicates statistical analyses. In design of experiments, missing values are particularly problematic when constructing optimal designs, as it is not known which values are missing at the design stage. When data are missing at random it is possible to incorporate this information into the optimality criterion that is used to find designs; Imhof, Song, and Wong (...

When is missing data recoverable?

Suppose a non-random portion of a data vector is missing. With some minimal prior knowledge about the data vector, can we recover the missing portion from the available one? In this paper, we consider a linear programming approach to this problem, present numerical evidence suggesting the effectiveness and limitation of this approach, and give deterministic conditions that guarantee a successfu...