Datenneugewichtung
Datenneugewichtung, also known as data reweighting, is a statistical technique used to adjust the weights of data points in a dataset to better represent the underlying population or to correct for biases. This method is commonly employed in fields such as survey sampling, machine learning, and econometrics.
The primary goal of data reweighting is to ensure that the weighted dataset is representative of the
There are several methods for data reweighting, including:
1. Raking: This method involves iteratively adjusting the weights of different groups to match specified marginal
2. Calibration: This approach involves adjusting the weights to match the distribution of a set of calibration
3. Post-stratification: This method involves dividing the data into strata based on auxiliary variables and then
Data reweighting has several applications, including improving the accuracy of survey estimates, enhancing the performance of