Estimation from current-status data in continuous time

TY - JOUR

T1 - Estimation from current-status data in continuous time

AU - Keiding, Niels

AU - Begtrup, Kamilla

AU - Scheike, Thomas H.

AU - Hasibeder, Günther

PY - 1996/6

Y1 - 1996/6

N2 - The nonparametric maximum likelihood estimator for current-status data has been known for at least 40 years, but only recently have the mathematical-statistical properties been clarified. This note provides a case study in the important and often studied context of estimating age-specific immunization intensities from a seroprevalence survey. Fully parametric and spline-based alternatives (also based on continuous-time models) are given. The basic reproduction number R0 exemplifies estimation of a functional. The limitations implied by the necessarily rather restrictive epidemiological assumptions are briefly discussed.

AB - The nonparametric maximum likelihood estimator for current-status data has been known for at least 40 years, but only recently have the mathematical-statistical properties been clarified. This note provides a case study in the important and often studied context of estimating age-specific immunization intensities from a seroprevalence survey. Fully parametric and spline-based alternatives (also based on continuous-time models) are given. The basic reproduction number R0 exemplifies estimation of a functional. The limitations implied by the necessarily rather restrictive epidemiological assumptions are briefly discussed.

KW - Age-specific incidence

KW - Basic reproduction number

KW - Epidemiology

KW - Smoothing splines

KW - Weibull survival distribution

UR - http://www.scopus.com/inward/record.url?scp=0030321329&partnerID=8YFLogxK

U2 - 10.1007/BF00128570

DO - 10.1007/BF00128570

M3 - Journal article

C2 - 9384639

AN - SCOPUS:0030321329

SN - 1380-7870

VL - 2

SP - 119

EP - 129

JO - Lifetime Data Analysis

JF - Lifetime Data Analysis

IS - 2

ER -