A Collection of Techniques Correcting for Sample Selection Bias
Predicting outcomes using Costing.
Generate synthetic observations using inverse-probability weights
Predicting outcomes using non-parametric Inverse-Probability bagging
Plain replication of each observation by inverse-probability weights
Rejection Sampling is a method used in sambia's function 'costing' (Kr...
smoteMod is a modified version of the 'synthetic minority oversampling...
smoteNew is a necessary function that modifies the SMOTE algorithm.
Train a classifier via synthetic observations using inverse-probabilit...
A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.