Papers › Estimation and convergence rates in the distributional single index model
Estimation and convergence rates in the distributional single index model
Fadoua Balabdaoui, Alexander Henzi, Lukas Looser
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
The distributional single index model is a semiparametric regression model in which the conditional distribution functions P(Y ≤y | X = x) = F₀(θ₀(x), y) of a real-valued outcome variable Y depend on d-dimensional covariates X through a univariate, parametric index function θ₀(x), and increase stochastically as θ₀(x) increases. We propose least squares approaches for the joint estimation of θ₀ and F₀ in the important case where θ₀(x) = α₀^⊤x and obtain convergence rates of n^(-1/3), thereby improving an existing result that gives a rate of n^(-1/6). A simulation study indicates that the convergence rate for the estimation of α₀ might be faster. Furthermore, we illustrate our methods in a real data application that demonstrates the advantages of shape restrictions in single index models.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections