7 papers · 1 filter
An exact unbiased semi-parametric L2 quasi-likelihood framework, complete in the presence of ties
Landon Hurley
Maximum likelihood style estimators possesses a number of ideal characteristics, but require prior identification of the distribution of errors to ensure exact unbiasedness. Indepe…
An exact unbiased semi-parametric maximum quasi-likelihood framework which is complete in the presence of ties
Landon Hurley
This paper introduces a novel quasi-likelihood extension of the generalised Kendall \(τ_{a}\) estimator, together with an extension of the Kemeny metric and its associated covarian…
Completing and studentising Spearman's correlation in the presence of ties
Landon Hurley
Non-parametric correlation coefficients have been widely used for analysing arbitrary random variables upon common populations, when requiring an explicit error distribution to be…
Unbiased analytic non-parametric correlation estimators in the presence of ties
Landon Hurley
An inner-product Hilbert space formulation is defined over a domain of all permutations with ties upon the extended real line. We demonstrate this work to resolve the common first…
Studentising Kendall's Tau: U-Statistic Estimators and Bias Correction for a Generalised Rank Variance-Covariance framework
Landon Hurley
Kemeny (1959) introduced a topologically complete metric space to study ordinal random variables, particularly in the context of Condorcet's paradox and the measurability of ties.…
An unbiased non-parametric correlation estimator in the presence of ties
Landon Hurley
An inner-product Hilbert space formulation of the Kemeny distance is defined over the domain of all permutations with ties upon the extended real line, and results in an unbiased m…
An unbiased minimum variance non-parametric analytic and likelihood estimator for discrete and continuous score spaces
Landon Hurley
This manuscript develops a general purpose inner-product norm for the Kendall \(τ\) and Spearman's \(ρ\), which operates as an unbiased MLE even in the presence of ties. We derive…