Conditionally Acceptable Recentered Set Estimators

Set Operator Decomposition and Conditionally Translation Invariant Elementary Operators

In the first part, we recall the axiomatic definition of the elementary morphological operators (dilations, erosions, anti–dilations and anti–erosions) and their characterization in the case of Boolean lattices. This characterization is used to derive the set operator decompositions from the general decompositions of operators between complete lattices. In the second part, we define the notions...

Aggregating Conditionally Lexicographic Preferences Using Answer Set Programming Solvers

We consider voting over combinatorial domains, where alternatives are binary tuples. We assume that votes are specified as conditionally lexicographic preferences, or LP trees. We study aggregation problems of LP tree votes for several positional scoring rules. Our main goal is to demonstrate that answer-set programming can be effective in solving the winner and the evaluation problems for inst...

On Consistency of Estimators Based on Random Set Vector Observations

Comparing Interval Estimators for Reliability in a Dependent Set-up

In this paper some procedures for building confidence intervals for the reliability in stress-strength models are discussed and empirically compared. The particular case of a bivariate normal setup is considered. The confidence intervals suggested are obtained employing approximations or asymptotic properties of maximum likelihood estimators. The coverage and the precision of these intervals ar...

Hashed samples: selectivity estimators for set similarity selection queries

We study selectivity estimation techniques for set similarity queries. A wide variety of similarity measures for sets have been proposed in the past. In this work we concentrate on the class of weighted similarity measures (e.g., TF/IDF and BM25 cosine similarity and variants) and design selectivity estimators based on a priori constructed samples. First, we study the pitfalls associated with s...