Predictive inference, causal reasoning, and model assessment in nonparametric Bayesian analysis: a case study

E Arjas; A Andreev

doi:10.1023/a:1009620223494

Predictive inference, causal reasoning, and model assessment in nonparametric Bayesian analysis: a case study

Lifetime Data Anal. 2000 Sep;6(3):187-205. doi: 10.1023/a:1009620223494.

Authors

E Arjas¹, A Andreev

Affiliation

¹ Rolf Nevanlinna Institute, University of Helsinki, Finland. elja.arjas@rni.helsinki.fi

PMID: 10949858
DOI: 10.1023/a:1009620223494

Abstract

This paper continues our earlier analysis of a data set on acute ear infections in small children, presented in Andreev and Arjas (1998). The main goal here is to provide a method, based on the use of predictive distributions, for assessing the possible causal influence which the type of day care will have on the incidence of ear infections. A closely related technique is used for the assessment of the nonparametric Bayesian intensity model applied in the paper. Two graphical methods, supported by formal tests, are suggested for this purpose.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Bayes Theorem*
Child Day Care Centers*
Child, Preschool
Humans
Incidence
Infant
Models, Statistical*
Otitis / epidemiology*