Binary probit regression with I-priors

Abstract

An extension of the I-prior methodology to binary response data is explored. Starting from a latent variable approach, it is assumed that there exists continuous, auxiliary random variables which decide the outcome of the binary responses. Fitting a classical linear regression model on these latent variables while assuming normality of the error terms leads to the well-known generalised linear model with a probit link. A more general regression approach is considered instead, in which an I-prior on the regression function, which lies in some reproducing kernel Hilbert space, is assumed. An I-prior distribution is Gaussian with mean chosen a priori, and covariance equal to the Fisher information for the regression function. By working with I-priors, the benefits of the methodology are brought over to the binary case - one of which is that it provides a unified model-fitting framework that includes additive models, multilevel models and models with one or more functional covariates. The challenge is in the estimation, and a variational approximation is employed to overcome the intractable likelihood. Several real-world examples are presented from analyses conducted in R.

Date
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London, United Kingdom