Showing stat.MEShow all
2 papers · 1 filter
stat.ME2026
Semiparametric Uncertainty Quantification via Isotonized Posterior for Deconvolutions
Francesco Gili, Geurt Jongbloed
We address the problem of uncertainty quantification for the deconvolution model \(Z = X + Y\), where \(X\) and \(Y\) are nonnegative random variables and the goal is to estimate t…
stat.ME2024
Testing for no effect in regression problems: a permutation approach
MichaŠCiszewski, Jakob Söhl, Ton Leenen +2
Often the question arises whether can be predicted based on using a certain model. Especially for highly flexible models such as neural networks one may ask whether a seemi…