Abstract
In a semiparametric transformation model, an increasing transformation of the survival time is linearly related to a covariate Z with an error distribution ε — i.e., the survival time T has the property that α (T) = −θ z +ε given Z = z, where α is an unknown extended real-valued function on ℝ and θ is an unknown constant in ℝd. In this paper, we consider the estimation of the transformation function α and the regression coefficient θ when the survival time data are subjected to general censorship. An observation is said to be censored by a general censorship scheme if there are random intervals which would hide the observation when it falls inside them. In such cases, we see the censoring interval instead of the actual observation. The maximum likelihood method is used to estimate the unknown parameters, and the asymptotic properties of the estimators are studied.
Results in this paper were presented at the International Conference on Mathematical Modelling and Computation held at Universiti Brunei Darussalam during 5-8 June 2006. in conjunction with the 20th anniversary of the foundation of the university.
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Kumphon, B., Mangalam, V. (2008). Estimation for the Semiparametric Transformation Model under General Censorship. In: Hosking, R.J., Venturino, E. (eds) Aspects of Mathematical Modelling. Mathematics and Biosciences in Interaction. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8591-0_17
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