Monday, January 19, 2015

Maximum likelihood estimation for linear mixed models

João José Gomes

Abstract: A linear model is $Y=X\beta+\varepsilon$ where $Y$, the response vector, is Gau\ ssian $(X\beta,\sigma I)$. A mixed model incorporates two random variables: $B$ the random effects and $Y\ $ the response vector. In a linear mixed model the unconditional distribution of $B$ and the condition\ al distribution $(Y| B = b)$ are both Gaussian distributions, $(Y| B =b)\sim N(X\beta+Zb,\sigma I)$ and $B \sim N (0,\Sigma\theta)$.
Objective: Parameter estimation by maximum likelihood.
Problem: Understand the algorithm.

Monday, January 19, 2015
Time: 16h30
Room: A2-25, Complexo Interdisciplinar, UL