Likelihood and Bayesian inference and computation

Abstract

Most of this book concerns the interpretation of regression models, with the understanding that they can be fit to data fairly automatically using R and Bugs. However, it can be useful to understand some of the theory behind the model fitting, partly to connect to the usual presentation of these models in statistics and econometrics. This chapter outlines some of the basic ideas of likelihood and Bayesian inference and computation, focusing on their application to multilevel regression. One point of this material is to connect multilevel modeling to classical regression; another is to give enough insight into the computation to allow you to understand some of the practical computational tips presented in the next chapter. Least squares and maximum likelihood estimation We first present the algebra for classical regression inference, which is then generalized when moving to multilevel modeling. We present the formulas here without derivation; see the references listed at the end of the chapter for more. Least squares The classical linear regression model is y i = X i β + ∊ i , where y and ∊ are (column) vectors of length n , X is a n × k matrix, and β is a vector of length k . The vector β of coefficients is estimated so as to minimize the errors ∊ i .