Non‐parametric confidence estimates for the Gini–Simpson measure of sparsity. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Konstantinides, J.M.; Andreadis, I.
• Is Part Of: Electronics letters. Volume 54:Issue 1(2018)
• Page Start: 27
• Page End: 29
• Publication Date: 2018-01-01
• Subjects: signal processing -- estimation theory; nonparametric confidence estimation -- data sparsity estimation -- adaptive algorithm -- signal processing -- Gini‐Simpson measure of sparsity index; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2017.2522
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Assessment of the quality of local estimates of data sparsity is central for various adaptive algorithms in signal processing. Empirical bounds for the estimation performance of a frequently used measure of sparsity, namely the Gini–Simpson index are derived. Confidence bounds are derived for an unbiased estimator of this measure, with exponential convergence to the true (unknown) sparsity value, as the number of samples increases. The analysis is distribution‐free, as no parametric or distributional assumptions are made for the available data.