Mixtures of semiparametric varying coefficient models for longitudinal data with nonignorable dropout

Zhiqiang Li; Liugen Xue

doi:10.1007/s10255-009-7141-6

ScienceGate Book Chapters

JOURNAL ARTICLE

Mixtures of semiparametric varying coefficient models for longitudinal data with nonignorable dropout

Zhiqiang Li Liugen Xue

Year: 2009 Journal: Acta Mathematicae Applicatae Sinica English Series Vol: 26 (1)Pages: 125-132 Publisher: Springer Science+Business Media

DOI: 10.1007/s10255-009-7141-6

Get Full-Text PDF Get Analytical Report

Keywords:

Dropout (neural networks) Estimator Semiparametric regression Parametric statistics Semiparametric model Mathematics Applied mathematics Regression Longitudinal data Computer science Statistics Machine learning

Metrics

Cited By

0.76

FWCI (Field Weighted Citation Impact)

Refs

0.83

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Statistical Distribution Estimation and Applications

Physical Sciences → Mathematics → Statistics and Probability

Mixtures of semiparametric varying coefficient models for longitudinal data with nonignorable dropout

Metrics

Citation History

Topics

Related Documents

Empirical likelihood inference in mixtures of semiparametric varying coefficient EV models for longitudinal data with nonignorable dropout

Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout

A varying-coefficient method for analyzing longitudinal clinical trials data with nonignorable dropout

Marginalized models for longitudinal ordinal data with nonignorable dropout

Empirical likelihood inference in mixture of semiparametric varying-coefficient models for longitudinal data with non-ignorable dropout