Regression analysis of bivariate current status data under the proportional hazards model. Issue 4 (27th October 2017)

• Record Type: Journal Article
• Authors: Hu, Tao; Zhou, Qingning; Sun, Jianguo
• Is Part Of: Canadian journal of statistics. Volume 45:Issue 4(2017)
• Page Start: 410
• Page End: 424
• Publication Date: 2017-10-27
• Subjects: Bernstein polynomials -- copula model -- efficient estimation -- sieve estimation -- MSC 2010: Primary 62N01 -- secondary 62G20; Mathematical statistics -- Periodicals; 519.5
• DOI: 10.1002/cjs.11344
• Languages: English
• ISSNs: 0319-5724

Abstract

Abstract: This article discusses the regression analysis of bivariate current status or case I interval‐censored failure time data under the marginal proportional hazards model. Several estimation procedures have been proposed for this problem, but each method either applies only to limited situations or no theoretical justification has been provided. Using Bernstein polynomials and the copula model we develop a sieve maximum likelihood estimation approach that applies to more general situations. In particular this method leaves the underlying copula model completely unspecified and can be easily implemented. The proposed estimators are shown to be strongly consistent and the asymptotic normality and efficiency of the regression parameter estimator are established. Simulation studies are conducted to assess the performance of the proposed method. An illustrative example is also provided. The Canadian Journal of Statistics 45: 410–424; 2017 © 2017 Statistical Society of Canada