A new copula model-based method for regression analysis of dependent current status data

被引:5
|
作者
Cui, Qi [1 ]
Zhao, Hui [2 ,3 ]
Sun, Jianguo [1 ,4 ]
机构
[1] Jilin Univ, Sch Math, Changchun 130012, Jilin, Peoples R China
[2] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China
[3] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Hubei, Peoples R China
[4] Univ Missouri, Dept Stat, Columbia, MO 65211 USA
关键词
Copula model; Current status data; Informative censoring; Proportional hazards model; PROPORTIONAL HAZARDS MODEL; EFFICIENT ESTIMATION; SURVIVAL;
D O I
10.4310/SII.2018.v11.n3.a9
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper discusses regression analysis of current status data, which arise when the occurrence of the failure event of interest is observed only once or the occurrence time is either left- or right-censored [5, 11] . Many authors have investigated the problem, however, most of the existing methods are parametric or apply only to limited situations such that the failure time and the observation time have to be independent. In particular, Ma et al. [7] recently proposed a copula-based procedure for the situation where the failure time and the observation time are allowed to be dependent but their association needs to be known. To address this restriction, we present a new two-step estimation procedure that allows one to estimate the association parameter in addition to estimation of other unknown parameters. The asymptotic properties of the resulting estimators are established and a simulation study is conducted and suggests that the proposed method performs well for practical situations. Also an illustrative example is provided.
引用
收藏
页码:463 / 471
页数:9
相关论文
共 50 条
  • [21] A semiparametric regression cure model with current status data
    Lam, KF
    Xue, HQ
    [J]. BIOMETRIKA, 2005, 92 (03) : 573 - 586
  • [22] A New Approach for Regression Analysis of Multivariate Current Status Data with Informative Censoring
    Li, Huiqiong
    Ma, Chenchen
    Sun, Jianguo
    Tang, Niansheng
    [J]. COMMUNICATIONS IN MATHEMATICS AND STATISTICS, 2023, 11 (04) : 775 - 794
  • [23] A New Approach for Regression Analysis of Multivariate Current Status Data with Informative Censoring
    Huiqiong Li
    Chenchen Ma
    Jianguo Sun
    Niansheng Tang
    [J]. Communications in Mathematics and Statistics, 2023, 11 : 775 - 794
  • [24] Regression analysis of current status data with latent variables
    Chunjie Wang
    Bo Zhao
    Linlin Luo
    Xinyuan Song
    [J]. Lifetime Data Analysis, 2021, 27 : 413 - 436
  • [25] SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA
    Liang, Baosheng
    Tong, Xingwei
    Zeng, Donglin
    Wang, Yuanjia
    [J]. STATISTICA SINICA, 2017, 27 (03) : 1079 - 1100
  • [26] Regression analysis of current status data with latent variables
    Wang, Chunjie
    Zhao, Bo
    Luo, Linlin
    Song, Xinyuan
    [J]. LIFETIME DATA ANALYSIS, 2021, 27 (03) : 413 - 436
  • [27] Regression in a copula model for bivariate count data
    Nikoloulopoulos, Aristidis K.
    Karlis, Dimitris
    [J]. JOURNAL OF APPLIED STATISTICS, 2010, 37 (09) : 1555 - 1568
  • [28] Regression Analysis of Current Status Data Under the Additive Hazards Model with Auxiliary Covariates
    Feng, Yanqin
    Ma, Ling
    Sun, Jianguo
    [J]. SCANDINAVIAN JOURNAL OF STATISTICS, 2015, 42 (01) : 118 - 136
  • [29] Semiparametric probit regression model with misclassified current status data
    Fang, Lijun
    Li, Shuwei
    Sun, Liuquan
    Song, Xinyuan
    [J]. STATISTICS IN MEDICINE, 2023, 42 (24) : 4440 - 4457
  • [30] Analysis for partially accelerated dependent competing risks model with masked data based on copula function
    Li, Yue
    Ye, Jimin
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2024, 53 (02) : 1020 - 1036