Missing Data and Convenient Assumptions

Missing data and convenient assumptions.

T his issue contains the first of several planned statistical primers on methodological problems commonly encountered by outcomes researchers. Although several resources exist for the interested outcomes researcher, they are often scattered throughout different literatures and, in particular, are not translated to the " outcomes " setting. Much of the empirical basis of outcomes research involv...

Extracting Assumptions from Missing Data

Information integration is the task of aggregating data from multiple heterogeneous data sources. The understandings of context knowledge of data sources are often the keys to challenging problems in information integration such as handling missing and inconsistent data. Context logic provides a unified framework for the modeling of data sources; nevertheless, the acquisition of large amounts o...

Robustness to Parametric Assumptions in Missing Data Models

Suppose we have a random sample from a population of interest. For each sampled unit we observe the covariate X , which we assume is discrete with support {x1, . . . , xK}. For some units, we also observe the variable Y . Let D = 1 if we observe Y , and D = 0 otherwise. We are interested in the population mean of Y , θ = E[Y ] = ∑K k=1 pkμk, where μk = E[Y |X = xk] and pk = Pr(X = xk). We assum...

Moving beyond convenient assumptions on fertility, mortality and migration

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...