On the Bernstein-smoothed lower-tail Spearman's rho estimator
By: Frédéric Ouimet, Selim Orhun Susam
Potential Business Impact:
Measures how two things are related, even when rare.
This note develops a Bernstein estimator for lower-tail Spearman's rho and establishes its strong consistency and asymptotic normality under mild regularity conditions. Smoothing the empirical copula yields a strictly smaller mean squared error (MSE) in tail regions by lowering sampling variance relative to the classical Spearman's rho estimator. A Monte Carlo simulation experiment with the Farlie--Gumbel--Morgenstern copula demonstrates variance reductions that translate into lower MSE estimates (up to $\sim 70\%$ lower) at deep-tail thresholds under weak to moderate dependence and small sample sizes. To facilitate reproducibility of the findings, the R code that generated all simulation results is readily accessible online.
Similar Papers
Spearman's rho for bivariate zero-inflated data
Methodology
Measures how things are linked, even with lots of zeros.
Asymptotic representations for Spearman's footrule correlation coefficient
Statistics Theory
Helps measure how well two lists match.
The Behrens--Fisher problem revisited
Statistics Theory
Makes math tests fairer for different group sizes.