Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach

This paper is concerned with interval estimation for the breakpoint parameter in segmented regression. We present score-type confidence intervals derived from the score statistic itself and from the recently proposed gradient statistic. Due to lack of regularity conditions of the score, non-smoothness and non-monotonicity, naive application of the score-based statistics is unfeasible and we propose to exploit the smoothed score obtained via induced smoothing. We compare our proposals with the traditional methods based on the Wald and the likelihood ratio statistics via simulations and an analysis of a real dataset: results show that the smoothed score-like statistics perform in practice somewhat better than competitors, even when the model is not correctly specified. Score-type confidence intervals based on the induced smoothing framework are presented for the breakpoint parameter of segmented regression.