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A Stochastic EM Type Algorithm for Parameter Estimation in Models with Continuous Outcomes, under Complex Ascertainment
Stockholm University, Faculty of Science, Department of Mathematics.
Karolinska institutet.
Stockholm University, Faculty of Science, Department of Mathematics.
2010 (English)In: The International Journal of Biostatistics, ISSN 1557-4679, Vol. 6, no 1, Article 23- p.Article in journal (Refereed) Published
Abstract [en]

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.
Keyword [en]
ascertainment, stochastic EM algorithm, missing data, outcome-dependent sampling, genetic epidemiology
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:su:diva-49323DOI: 10.2202/1557-4679.1222OAI: oai:DiVA.org:su-49323DiVA: diva2:377162
Funder
Swedish Research Council, 523-2006-972Swedish Research Council, 621-2005-2810
Available from: 2010-12-13 Created: 2010-12-13 Last updated: 2010-12-15Bibliographically approved
In thesis
1. Design and analysis of response selective samples in observational studies
Open this publication in new window or tab >>Design and analysis of response selective samples in observational studies
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Outcome dependent sampling may increase efficiency in observational studies. It is however not always obvious how to sample efficiently, and how to analyze the resulting data without introducing bias. This thesis describes a general framework for efficiency calculations in multistage sampling, with focus on what is sometimes referred to as ascertainment sampling. A method for correcting for the sampling scheme in analysis of ascertainment samples is also presented. Simulation based methods are used to overcome computational issues in both efficiency calculations and analysis of data.

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2011. 68 p.
Keyword
ascertainment, missing data, outcome dependent sampling, response selective samples, sequential design, stochastic EM algorithm
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-49328 (URN)978-91-7447-201-1 (ISBN)
Public defence
2011-02-04, sal 14, hus 5, Kräftriket, Roslagsvägen 101, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
At the time of doctoral defense, the following paper was unpublished and had a status as follows: Paper 1: Submitted.Available from: 2011-01-13 Created: 2010-12-13 Last updated: 2011-05-26Bibliographically approved

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