A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics
We introduce tools for controlled variable selection to economists. In particular, we apply a recently introduced aggregation scheme for false discovery rate (FDR) control to German administrative data to determine the parts of the individual employment histories that are relevant for the career outcomes of women. Our results suggest that career outcomes can be predicted based on a small set of variables, such as daily earnings, wage increases in combination with a high level of education, employment status, and working experience.
Code (0)
등록된 구현이 없습니다.
Tasks
Variable SelectionSimilar Papers 제목 키워드 기반
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Gaussian graphical models emerge in a wide range of fields. They model the statistical relationships between variables as a graph, where an edge between two variables indicates conditional dependence. Unfortunately, well…
Graph LearningVariable SelectionVariable selection with false discovery rate control in deep neural networks
Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting t…
Variable SelectionDifferentially Private Variable Selection via the Knockoff Filter
The knockoff filter, recently developed by Barber and Candes, is an effective procedure to perform variable selection with a controlled false discovery rate (FDR). We propose a private version of the knockoff filter by i…
Variable SelectionPruning variable selection ensembles
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensembl…
DiversityEnsemble LearningVariable SelectionSparse PCA with False Discovery Rate Controlled Variable Selection
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the double duty of dimension reduction and v…
Dimensionality ReductionVariable Selection