You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Bivariate Current Status Data with Univariate Monitoring Times
Nicholas P. Jewell, Mark Van Der Laan and Xiudong Lei
Vol. 92, No. 4 (Dec., 2005), pp. 847-862
Stable URL: http://www.jstor.org/stable/20441240
Page Count: 16
You can always find the topics here!Topics: Statistical estimation, Estimators, Maximum likelihood estimators, Maximum likelihood estimation, Statistical variance, Simulations, Estimation bias, Sampling bias, Jewelry, Weighting functions
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
For bivariate current status data with univariate monitoring times, the identifiable part of the joint distribution is three univariate cumulative distribution functions, namely the two marginal distributions and the bivariate cumulative distribution function evaluated on the diagonal. We show that smooth functionals of these univariate cumulative distribution functions can be efficiently estimated with easily computed nonparametric maximum likelihood estimators based on reduced data consisting of univariate current status observations. This theory is then applied to functionals that address independence of the two survival times and the goodness-of-fit of a copula model used by Wang & Ding (2000). Some brief simulations are provided along with an illustration based on data on HIV transmission. Extension of the ideas to incorporate covariates, possibly time-dependent, are discussed.
Biometrika © 2005 Biometrika Trust