Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty


Autoria(s): Shen, Xia; Rönnegård, Lars
Data(s)

2010

Resumo

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.

Formato

application/pdf

Identificador

http://urn.kb.se/resolve?urn=urn:nbn:se:du-4840

Idioma(s)

eng

Publicador

Högskolan Dalarna, Statistik

Högskolan Dalarna, Statistik

Shanghai, P. R. China

Direitos

info:eu-repo/semantics/openAccess

Tipo

Conference paper

info:eu-repo/semantics/conferenceObject

text