Universal Inference Meets Random Projections: A Scalable Test for Log-concavity

arXiv:2111.09254v4 Announce Type: replace-cross
Abstract: Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concavity is motivated by applications across economics, survival modeling, and reliability theory. However, there do not currently exist valid tests for whether the underlying density of given data is log-concave. The recent universal inference methodology provides a valid test. The universal test relies on maximum likelihood estimation (MLE), and efficient methods already exist …

abstract applications arxiv constraints cs.lg data economics however inference math.st modeling parametric random reliability scalable stat.me stat.th survival test tests theory type universal