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A Stochastic EM Type Algorithm for Parameter Estimation in Models with Continuous Outcomes, under Complex Ascertainment
Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
Karolinska institutet.
Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
2010 (Engelska)Ingår i: The International Journal of Biostatistics, E-ISSN 1557-4679, Vol. 6, nr 1, s. Article 23-Artikel i tidskrift (Refereegranskat) 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. 

Ort, förlag, år, upplaga, sidor
2010. Vol. 6, nr 1, s. Article 23-
Nyckelord [en]
ascertainment, stochastic EM algorithm, missing data, outcome-dependent sampling, genetic epidemiology
Nationell ämneskategori
Sannolikhetsteori och statistik
Forskningsämne
matematisk statistik
Identifikatorer
URN: urn:nbn:se:su:diva-49323DOI: 10.2202/1557-4679.1222OAI: oai:DiVA.org:su-49323DiVA, id: diva2:377162
Forskningsfinansiär
Vetenskapsrådet, 523-2006-972Vetenskapsrådet, 621-2005-2810Tillgänglig från: 2010-12-13 Skapad: 2010-12-13 Senast uppdaterad: 2024-02-27Bibliografiskt granskad
Ingår i avhandling
1. Design and analysis of response selective samples in observational studies
Öppna denna publikation i ny flik eller fönster >>Design and analysis of response selective samples in observational studies
2011 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: Department of Mathematics, Stockholm University, 2011. s. 68
Nyckelord
ascertainment, missing data, outcome dependent sampling, response selective samples, sequential design, stochastic EM algorithm
Nationell ämneskategori
Sannolikhetsteori och statistik
Forskningsämne
matematisk statistik
Identifikatorer
urn:nbn:se:su:diva-49328 (URN)978-91-7447-201-1 (ISBN)
Disputation
2011-02-04, sal 14, hus 5, Kräftriket, Roslagsvägen 101, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Anmärkning
At the time of doctoral defense, the following paper was unpublished and had a status as follows: Paper 1: Submitted.Tillgänglig från: 2011-01-13 Skapad: 2010-12-13 Senast uppdaterad: 2022-02-24Bibliografiskt granskad

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Förlagets fulltexthttp://www.bepress.com/ijb/vol6/iss1/23

Person

Grünewald, MariaHössjer, Ola

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The International Journal of Biostatistics
Sannolikhetsteori och statistik

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Totalt: 71 träffar
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