Empirical analysis of current status data for additive hazards model with auxiliary covariates

Jianling Zhang; Mei Yang; Xiuqing Zhou

Kybernetika (2021)

  • Volume: 57, Issue: 5, page 801-818
  • ISSN: 0023-5954

Abstract

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In practice, it often occurs that some covariates of interest are not measured because of various reasons, but there may exist some auxiliary information available. In this case, an issue of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses statistical inference problems in the context of current status data arising from an additive hazards model with auxiliary covariates. An empirical log-likelihood ratio statistic for the regression parameter vector is defined and its limiting distribution is shown to be a standard chi-squared distribution. A profile empirical log-likelihood ratio statistic for a sub-vector of the parameters and its asymptotic distribution are also studied. To assess the finite sample performance of the proposed methods, simulation studies are implemented and simulation results show that the methods work well.

How to cite

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Zhang, Jianling, Yang, Mei, and Zhou, Xiuqing. "Empirical analysis of current status data for additive hazards model with auxiliary covariates." Kybernetika 57.5 (2021): 801-818. <http://eudml.org/doc/297832>.

@article{Zhang2021,
abstract = {In practice, it often occurs that some covariates of interest are not measured because of various reasons, but there may exist some auxiliary information available. In this case, an issue of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses statistical inference problems in the context of current status data arising from an additive hazards model with auxiliary covariates. An empirical log-likelihood ratio statistic for the regression parameter vector is defined and its limiting distribution is shown to be a standard chi-squared distribution. A profile empirical log-likelihood ratio statistic for a sub-vector of the parameters and its asymptotic distribution are also studied. To assess the finite sample performance of the proposed methods, simulation studies are implemented and simulation results show that the methods work well.},
author = {Zhang, Jianling, Yang, Mei, Zhou, Xiuqing},
journal = {Kybernetika},
keywords = {current status data; auxiliary covariates; additive hazards model; empirical likelihood},
language = {eng},
number = {5},
pages = {801-818},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Empirical analysis of current status data for additive hazards model with auxiliary covariates},
url = {http://eudml.org/doc/297832},
volume = {57},
year = {2021},
}

TY - JOUR
AU - Zhang, Jianling
AU - Yang, Mei
AU - Zhou, Xiuqing
TI - Empirical analysis of current status data for additive hazards model with auxiliary covariates
JO - Kybernetika
PY - 2021
PB - Institute of Information Theory and Automation AS CR
VL - 57
IS - 5
SP - 801
EP - 818
AB - In practice, it often occurs that some covariates of interest are not measured because of various reasons, but there may exist some auxiliary information available. In this case, an issue of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses statistical inference problems in the context of current status data arising from an additive hazards model with auxiliary covariates. An empirical log-likelihood ratio statistic for the regression parameter vector is defined and its limiting distribution is shown to be a standard chi-squared distribution. A profile empirical log-likelihood ratio statistic for a sub-vector of the parameters and its asymptotic distribution are also studied. To assess the finite sample performance of the proposed methods, simulation studies are implemented and simulation results show that the methods work well.
LA - eng
KW - current status data; auxiliary covariates; additive hazards model; empirical likelihood
UR - http://eudml.org/doc/297832
ER -

References

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