Maximum Likelihood Estimator for Semiparametric Transformation Model under General Censorship

Authors

  • Bungon Kumphon Department of Mathematics, Faculty of Science, Mahasarakham University, Mahasarakham 44150, Thailand.
  • Prayad Sangngam Department of Statistics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand.

Keywords:

censored data, interval censoring, semiparametric model, transformation model

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 . In other words, the survival time T has the property that \alpha(T) =−θz + ε given Z = z , where \alpha is an unknown extended real-valued function on R and θ is an unknown constant in Rd . An observation is said to be censored by a general censorship scheme if there are random intervals which, when the observation falls inside them, would hide it. In such cases we get the censoring interval instead of the actual observation. In this paper we consider maximum likelihood estimation of the transformation function α and the regression coefficient θ when the survival time data are subjected to general censorship.

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How to Cite

Kumphon, B., & Sangngam, P. (2015). Maximum Likelihood Estimator for Semiparametric Transformation Model under General Censorship. Thailand Statistician, 5, 81–92. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/34356

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