Combining Non-Probability and Probability Survey Samples Through Mass Imputation

Combining Multiple Imputation and Inverse-Probability Weighting

Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW). IPW is also used to adjust for unequal sampling fractions. MI is generally more efficient than IPW but more complex. Whereas IPW requires only a model for the probability that an individual has complete data (a univariate outcome), MI needs a model for the joint distribut...

Imputation using response probability

The authors propose a new ratio imputation method using response probability. Their estimator can be justified either under the response model or under the imputation model; it is thus doubly protected against the failure of either of these models. The authors also propose a variance estimator that can be justified under the two models. Their methodology is applicable whether the response proba...

Non-zero probability of nearest neighbor searching

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, suc...

Combining Probability Forecasts

Linear pooling is by the far the most popular method for combining probability forecasts. However, any nontrivial weighted average of two or more distinct, calibrated probability forecasts is necessarily uncalibrated and lacks sharpness. In view of this, linear pooling requires recalibration, even in the ideal case in which the individual forecasts are calibrated. Toward this end, we propose a ...

Semanti Domains for Combining Probability and Non-Determinism