Abstract
We consider the intractable posterior density that results when the one-way logistic analysis of variance model is combined with a flat prior. We analyze Polson, Scott and Windle’s (2013) data augmentation (DA) algorithm for exploring the posterior. The Markov operator associated with the DA algorithm is shown to be trace-class.
Highlights
Of unknown regression coefficients, and F is the standard logistic distribution function, that is, F (s) = es/(1 + es)
An important special case is the one-way logistic analysis of variance (ANOVA) model, where each xi is a unit vector. (See Section 3 for a detailed explanation of how the logistic regression model reduces to the one-way model.) In general, the joint mass function of Y1, . . . , YN is given by
We may resort to Markov chain Monte Carlo (MCMC) methods to approximate the intractable posterior expectations
Summary
We show that in the one-way logistic ANOVA model, which is an important special case of logistic regression models, the Markov operator associated with PS& W’s DA algorithm is trace-class (see Section 3 for definition). We consider the posterior density that results when the logistic regression likelihood (1) is combined with a flat prior on the regression parameter β.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
