This paper introduces a unified profile quasi-likelihood framework for generalised linear models built on a topologically complete extension to Kendall's $τ$ with finite sample power and Type-I error rates. By establishing equivalence between continuous $L^2$ profile estimators and discrete Generalized Gibbs exponential families, we prove local asymptotic minimax efficiency over the copula tangent space. The framework yields monotone-affine invariant inference, sub-Gaussian concentration bounds, and exact finite-sample $t$-pivot tail majorization under arbitrary tie configurations. We illustrate the methodology through three canonical applications: bivariate rank correlation, paired testing, and intraclass correlation.

翻译：暂无翻译