Maximum likelihood estimation in semiparametric regression models with censored data
By Donglin Zeng, Danyu Lin. Statistica sinica, 2007
358 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f77d72183c38a77f01c3bfd81e29ba4aa7c6ab01
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Eric B. Laber
- Peter Buhlmann
- Jianqing Fan
- Michael R. Kosorok
- Nicolai Meinshausen
- Cun-Hui Zhang
- Thomas H. Scheike
- Sara A. van de Geer
Citations in the corpus
- Handbook of survival analysis (2014, background): “…3.2 (left display) that displays the Kaplan-Meier estimates of survival probabilities of the chemotherapy and chemotherapy plus radiotherapy groups of gastric cancer patients in a randomized clinical trial (Stablein and Koutrouvelis, 1985); these data were also considered in Zeng and Lin (2007b).”
- Sure independence screening in generalized linear models with NP-dimensionality (2009, background): “…can be applied to a rich class of general regression models, including transformation models [Box and Cox (1964); Bickel and Doksum (1981)], censored regression models [Cox (1972); Kosorok, Lee and Fine (2004); Zeng and Lin (2007)] and projection pursuit regression [Friedman and Stuetzle (1981)].”
- Semiparametric marginal regression analysis for dependent competing risks under an assumed copula (2010, methodology): “As in the case of independent censoring (Zeng and Lin, 2006, 2007; Chen, 2009), when deriving the non-parametric maximum likelihood estimator from log-likelihood (3), we view Rk.·/ as a non-decreasing step function with jumps only at the time points where event k is observed, k =1, 2, i.e. we…”
- Doubly Robust Learning for Estimating Individualized Treatment with Censored Data (2014, methodology): “Other working models can also be applied, such as transformation models (Zeng & Lin, 2007), nonparametric methods based on kernel type estimators (Dabrowska, 1989), or machine learning techniques (Zhu & Kosorok, 2012).”
- Most Likely Transformations (2015, methodology): “An overview of the extensive literature on the simultaneous non-parametric maximum likelihood estimation of hY and hx, i.e. estimation procedures not requiring an explicit parameterisation of hY , for censored continuous responses is given in Zeng and Lin (2007).”