Asymptotic properties of keys and functional dependencies in random databases
Theoretical Computer Science - Special issue: database theory
Average Case Analysis of Algorithms on Sequences
Average Case Analysis of Algorithms on Sequences
Entity-Relationship Modeling: Foundations of Database Technology
Entity-Relationship Modeling: Foundations of Database Technology
The Average Length of Keys and Functional Dependencies in (Random) Databases
ICDT '95 Proceedings of the 5th International Conference on Database Theory
Database Systems: An Application Oriented Approach, Complete Version (2nd Edition)
Database Systems: An Application Oriented Approach, Complete Version (2nd Edition)
Statistical inference for the ε-entropy and the quadratic Rényi entropy
Journal of Multivariate Analysis
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Numerous entropy-type characteristics (functionals) generalizing Rényi entropy are widely used in mathematical statistics, physics, information theory, and signal processing for characterizing uncertainty in probability distributions and distribution identification problems. We consider estimators of some entropy (integral) functionals for discrete and continuous distributions based on the number of epsilon-close vector records in the corresponding independent and identically distributed samples from two distributions. The proposed estimators are generalized U-statistics. We show the asymptotic properties of these estimators (e.g., consistency and asymptotic normality). The results can be applied in various problems in computer science and mathematical statistics (e.g., approximate matching for random databases, record linkage, image matching). AMS 2000 subject classification: 94A15, 62G20