IntroductionUnivariate and bivariate current‐status data are often encountered in medical, epidemiological, public health, and economical studies (Sun, ). Nonparametric maximum likelihood estimations for univariate current‐status data have long been studied (Barlow et al., ), while various semiparametric regression models have been carefully investigated (Rossini and Tsiatis, ; Dinse and Lagakos, ; Mcmahan et al., , among others).Recently, the analysis of bivariate current‐status survival data has drawn considerable interests. For example, Wang et al. () considered the joint regression analysis of times to the infections due to chlamydia and gonorrhea, respectively, which were observed as bivariate current‐status survival data. Existing statistical methods includes the nonparametric estimations (Wang and Ding, ; Ding and Wang, ; Jewell et al., ; Groeneboom et al., ; Sun et al., ), semiparametric models (Sun and Shen, ; Wang et al., ; Wen and Chen, ; Wang et al., ), and the Bayesian approaches (Dunson and Dinse, ), where additional motivational examples on bivariate current‐status survival data can be found. Despite the research effort, there is a great need for novel semiparametric regression models for analyzing multivariate current‐status data.Motivated by these examples of bivariate current‐status survival data in literature, we propose the maximum likelihood estimation under
Biometrics – Wiley
Published: Jan 1, 2018
Keywords: ; ; ; ;
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