Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Frontier estimation appears in productivity analysis. Firm’s performance is measured by the distance between its output and an optimal production frontier. Frontier estimation becomes difficult if outputs are measured with noise and most approaches rely on restrictive parametric assumptions. This paper contributes to nonparametric approaches, with unknown frontier and unknown variance of a normally distributed error. We propose a nonparametric method identifying and estimating both quantities simultaneously. Consistency and rate of convergence of our estimators are established, and simulations verify the performance of the estimators for small samples. We illustrate our method with data on American electricity companies.