6533b835fe1ef96bd12a0124
RESEARCH PRODUCT
Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach
Vito M. R. Muggeosubject
Statistics and Probability010504 meteorology & atmospheric sciencesInterval estimationBreakpointinduced smoothingScore01 natural sciencesConfidence intervalchangepoint010104 statistics & probabilitypiecewise linear relationshipconfidence intervalscore inferenceStatistics0101 mathematicsStatistics Probability and UncertaintySegmented regressionSettore SECS-S/01 - StatisticaStatisticSmoothing0105 earth and related environmental sciencesMathematicsdescription
Summary 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-09-01 | Australian & New Zealand Journal of Statistics |