학술논문

Log-Concavity of Multinomial Likelihood Functions Under Interval Censoring Constraints on Frequencies or Their Partial Sums
Document Type
Working Paper
Source
Subject
Mathematics - Statistics Theory
Computer Science - Machine Learning
Statistics - Machine Learning
Language
Abstract
We show that the likelihood function for a multinomial vector observed under arbitrary interval censoring constraints on the frequencies or their partial sums is completely log-concave by proving that the constrained sample spaces comprise M-convex subsets of the discrete simplex.
Comment: 7 pages