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Analysis of failure time using threshold regression with semi‐parametric varying coefficients
Author(s) -
Li Jialiang,
Lee MeiLing Ting
Publication year - 2011
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2011.00481.x
Subject(s) - interpretability , parametric statistics , inverse gaussian distribution , mathematics , stability (learning theory) , parametric model , regression analysis , regression , semiparametric model , proportional hazards model , statistics , computer science , econometrics , distribution (mathematics) , artificial intelligence , machine learning , mathematical analysis
Many new statistical models may enjoy better interpretability and numerical stability than traditional models in survival data analysis. Specifically, the threshold regression (TR) technique based on the inverse Gaussian distribution is a useful alternative to the Cox proportional hazards model to analyse lifetime data. In this article we consider a semi‐parametric modelling approach for TR and contribute implementational and theoretical details for model fitting and statistical inferences. Extensive simulations are carried out to examine the finite sample performance of the parametric and non‐parametric estimates. A real example is analysed to illustrate our methods, along with a careful diagnosis of model assumptions.