4 papers
Asymptotic properties of the MLE in distributional regression under random censoring
Gitte Kremling, Gerhard Dikta
Distributional regression aims to find the best candidate in a given parametric family of conditional distributions to model a given dataset. As each candidate in the distribution…
Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
Gitte Kremling, Jeffrey Näf, Johannes Lederer
The prevalence of missing values in data science poses a substantial risk to any further analyses. Despite a wealth of research, principled nonparametric methods to deal with gener…
Non-asymptotic error bounds for probability flow ODEs under weak log-concavity
Gitte Kremling, Francesco Iafrate, Mahsa Taheri +1
Score-based generative modeling, implemented through probability flow ODEs, has shown impressive results in numerous practical settings. However, most convergence guarantees rely o…
Bootstrap-Based Goodness-of-Fit Test for Parametric Families of Conditional Distributions
Gitte Kremling, Gerhard Dikta
A consistent goodness-of-fit test for distributional regression is introduced. The test statistic is based on a process that traces the difference between a nonparametric and a sem…