FURTHER DETAILS ON INFERENCE UNDER RIGHT CENSORING FOR TRANSFORMATION MODELS WITH A CHANGE-POINT BASED ON A COVARIATE THRESHOLD

By Michael R. Kosorok, Rui Song. 2006

83 citations recorded by the source corpus.

We consider linear transformation models applied to right censored survival data with a change-point based on a covariate threshold. We establish consistency and weak convergence of the nonparametric maximum lieklihood estimators. The change-point parameter is shown to be $n$-consistent, while the remaining parameters are shown to have the expected root-$n$ consistency. We show that the procedure is adaptive in the sense that the non-threshold parameters are estimable with the same precision as if the true threshold value were known. We also develop Monte-Carlo methods of inference for model parameters and score tests for the existence of a change-point. A key difficulty here is that some of the model parameters are not identifiable under the null hypothesis of no change-point. Simulation students establish the validity of the proposed score tests for finite sample sizes.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7a00d54d500ee0f53da04a6702c902082a576757

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Citations in the corpus

Testing for Threshold Effects in Regression Models (2010, background): “More recently, threshold models have been considered for nonparametric models (e.g., Delgado and Hidalgo 2000), for panel data models (e.g., Hansen 1999), for transformation models (e.g., Pons 2003; Kosorok and Song 2007), and for binary response models (e.g., Lee and Seo 2008), among others.”
Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling (2016, background): “The Davies’ approach is quite influential and many authors have used it in different contexts, see for instance Hansen [9] for an econometric perspective with simulation-based solutions, Kosorok and Song [10] for theoretical settings in survival analysis, and Zheng and Chen [11] for a review of gene”