Predictive Influence in the Log Normal Survival Model

Wesley O. Johnson
Additional contact information
Wesley O. Johnson: University of California, Division of Statistics

A chapter in Modelling and Prediction Honoring Seymour Geisser, 1996, pp 104-121 from Springer

Abstract: Abstract We discuss case deletion diagnostics for prediction of future observations in the log normal survival analysis model. The point of view taken is that prediction is the primary inferential goal in a survival analysis setting and that particular observations in the sample may be influential with regard to that goal. We thus consider the Kullback-Leibler divergence as a measure of the discrepancy between predictive densities based on full and case deleted samples. A large Kullback-Leibler number for a particular case is an indication that deletion of that case may result in substantially different predictive inferences.

Keywords: Monte Carlo Sample; Monte Carlo Approximation; Prior Observation; Predictive Density; Accelerate Failure Time Model (search for similar items in EconPapers)
Date: 1996
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2414-3_6

Ordering information: This item can be ordered from
http://www.springer.com/9781461224143

DOI: 10.1007/978-1-4612-2414-3_6

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().