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Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0003-4390-1979
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-1057-5401
2010 (engelsk)Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

sted, utgiver, år, opplag, sider
Shanghai, P. R. China, 2010.
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-4840OAI: oai:dalea.du.se:4840DiVA, id: diva2:522142
Konferanse
19th International Workshop on Matrices and Statistics , Shanghai, P. R. China, 5-8 juni, 2010
Tilgjengelig fra: 2010-06-18 Laget: 2010-06-18 Sist oppdatert: 2015-06-16bibliografisk kontrollert

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