# Semiparametric estimation of marginal mark distribution

@article{Huang2006SemiparametricEO, title={Semiparametric estimation of marginal mark distribution}, author={Yijian Huang and Kristin M. Berry}, journal={Biometrika}, year={2006}, volume={93}, pages={895-910} }

In many applications, the outcome of interest is a mark such that its observation is contingent upon occurrence of an event. With incomplete follow-up data, the marginal mark distribution is, however, nonparametrically nowhere identifiable in many practical situations. To address this problem, we suggest a semiparametric model that postulates a normal copula for the association between the mark and survival time, but leaves the marginals unspecified. We show identifiability of the marginal mark…

## 7 Citations

### Semiparametric estimation in copula models for bivariate sequential survival times

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This work model the joint distribution of the successive survival times by using copula functions, and provides semiparametric estimation procedures in which copula parameters are estimated without parametric assumptions on the marginal distributions, which provides more robust estimates and checks on the fit of parametric models.

### Nonparametric Two‐Sample Tests of the Marginal Mark Distribution with Censored Marks

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This work proposes two new families of two-sample tests for the null hypothesis of no difference in mark-scale distribution that allows for arbitrary associations between mark and time and suggests that the proposed rank-based tests can be nearly twice as powerful as linear tests.

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This approach introduces a shared frailty to specify the explicit dependence structure among the markers, the recurrent and terminal events, and a prediction of the covariate-specific cumulative markers is provided.

### Modelling the type and timing of consecutive events: application to predicting preterm birth in repeated pregnancies

- MathematicsJournal of the Royal Statistical Society. Series C, Applied statistics
- 2015

A joint model is proposed, where types of adverse outcomes across repeated pregnancies are modelled by using a polychotomous logistic regression model with random effects, and gestational ages at delivery areModelled conditionally on the types of adversarial outcome.

### Double robust estimator of average causal treatment effect for censored medical cost data

- MathematicsStatistics in medicine
- 2016

A double robust estimator is proposed for average causal treatment effect for right censored medical cost data that is double robust in the sense that it remains consistent when either the model for the treatment assignment or the regression models for the response is correctly specified.

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