๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Nonparametric Bayes estimation of a distribution function with truncated data

โœ Scribed by Mauro Gasparini

Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
411 KB
Volume
55
Category
Article
ISSN
0378-3758

No coin nor oath required. For personal study only.

โœฆ Synopsis

A truncation bias affects the observation of a pair of variables (X, Y), so that data are available only if Y ~<X. In such a situation, the nonparametric maximum likelihood estimator (NPMLE) of the distribution function of Y may have unpleasant features (Woodroofe, Ann. Statisr 13 (1985) 163-177). As a possible alternative, a nonparametric Bayes estimator is obtained using a Dirichlet prior (Ferguson, Ann. Statist. 1 (1973) 209-230). lts frequentist asymptotic behavior is investigated and found to be the same as the asymptotic behavior of the NPMLE. The results are illustrated by an example, with astronomical data, where the NPMLE is clearly unacceptable.

๐Ÿ“œ SIMILAR VOLUMES

Nonparametric Estimation of the Bivariat
Nonparametric Estimation of the Bivariate Survival Function with Truncated Data
โœ Mark J. van der Laan ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 766 KB

Randomly left or right truncated observations occur when one is concerned with estimation of the distribution of time between two events and when one only observes the time if one of the two events falls in a fixed time-window, so that longer survivial times have higher probability to be part of the