This paper studies the problem of rank aggregation under the Plackett-Luce model. The goal is to infer a global ranking and related scores of the items, based on partial rankings provided by multiple users over multiple subsets of items. A question of particular interest is how to optimally assign items to users for ranking and how many item assignments are needed to achieve a target estimation...

In this paper we introduce a new bound on an estimator’s error, derived from the classical Cramér-Rao Lower Bound (CRLB), for cases where the support of the likelihood function (LF) exhibits parameter-dependence. Parameterdependent support of the LF arises here when an unknown parameter is observed in the presence of additive measurement noise and the measurement noise pdf has a finite support....

In this paper, we explore a sequential Bayesian bound for state-space models focusing on hybrid continuous and discrete random states. We provide an analytic recursion for the sequential Weiss–Weinstein (SWW) bound for linear state-space models with solutions for Gaussian, uniform, and exponential distributions as derived, as well as for a combination of these. We compare the SWW bound for disc...