title: A non-parametric maximum likelihood estimator for bivariate interval censored data.
abstract: We derive a non-parametric maximum likelihood estimator for bivariate interval censored data using standard techniques for constrained convex optimization. Our approach extends those taken for univariate interval censored data. We illustrate the estimator with bivariate data from an AIDS study.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA. betensky@hsph.harvard.edu
authors.forename: R A
authors.initials: RA
authors.lastname: Betensky
authors.forename: D M
authors.initials: DM
authors.lastname: Finkelstein
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000150:complications; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003587:Cytomegalovirus / Q000302:isolation & purification; D003586:Cytomegalovirus Infections / Q000097:blood / Q000150:complications / Q000652:urine; D005243:Feces / Q000382:microbiology; D006801:Humans; D016013:Likelihood Functions; D015269:Mycobacterium avium Complex / Q000302:isolation & purification; D015270:Mycobacterium avium-intracellulare Infection / Q000150:complications; D013183:Sputum / Q000382:microbiology; D018709:Statistics, Nonparametric