Semiparametric analysis of transformation models with censored data

By Kani Chen, Zhezhen Jin, Zhiliang Ying. 2002

333 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/090dbebf8f2ef9fd99606583fee54efd6211d835

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In Artificial Intelligence

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Citations in the corpus

Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure (2005, methodology): “, n, the parameter /3 in the linear transformation model can be estimated using methods like the one proposed by Chen et al. (2002). If a patient dies and the cause of failure is unknown, classification is uncertain.”
On semiparametric transformation cure models (2004, methodology): “If y were known, the two equations could be regarded as extensions of the estimating equations introduced in Chen et al. (2002) for the usual transformation models without the cure fraction.”
Most Likely Transformations (2015, methodology): “When will the new method be more preferable than the alternative methods in the literature, such as Cheng et al. (1995), Chen et al. (2002)?”
Maximum likelihood estimation in the proportional hazards cure model (2008, methodology): “Their approach was motivated by the work of Chen et al. (2002) and used the martingale integral representation to construct unbiased estimating equations.”
Maximum likelihood estimation in semiparametric regression models with censored data (2007, methodology): “Dabrowska and Doksum (1988), Cheng et al. (1995) and Chen et al. (2002) proposed general estimators for this class of models, none of which are asymptotically efficient.”
Maximum Likelihood Estimation for the Proportional Odds Model With Random Effects (2005, positive): “Important contributions have been made by Bennett (1983), Pettitt (1984), Cuzick (1988), Dabrowska and Doksum (1988), Cheng, Wei, and Ying (1995), Wu (1995), Murphy, Rossini, and van der Vaart (1997), Shen (1998), Lam and Leung (2001), and Chen, Jin, and Ying (2002), among others.”