A Stochastic EM Type Algorithm for Parameter Estimation in Models with Continuous Outcomes, under Complex Ascertainment
2010 (English)In: The International Journal of Biostatistics, ISSN 1557-4679, Vol. 6, no 1, Article 23- p.Article in journal (Refereed) Published
Outcome-dependent sampling probabilities can be used to increase efficiency in observational studies. For continuous outcomes, appropriate consideration of sampling design in estimating parameters of interest is often computationally cumbersome. In this article, we suggest a Stochastic EM type algorithm for estimation when ascertainment probabilities are known or estimable. The computational complexity of the likelihood is avoided by filling in missing data so that an approximation of the full data likelihood can be used. The method is not restricted to any specific distribution of the data and can be used for a broad range of statistical models.
Place, publisher, year, edition, pages
2010. Vol. 6, no 1, Article 23- p.
ascertainment, stochastic EM algorithm, missing data, outcome-dependent sampling, genetic epidemiology
Probability Theory and Statistics
Research subject Mathematical Statistics
IdentifiersURN: urn:nbn:se:su:diva-49323DOI: 10.2202/1557-4679.1222OAI: oai:DiVA.org:su-49323DiVA: diva2:377162
FunderSwedish Research Council, 523-2006-972Swedish Research Council, 621-2005-2810