Bayesian variable and link determination for generalised linear models

100 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012, methodology): “See also Ehlers and Brooks (2003) for an extension to a more general setting, and Ntzoufras et al. (2003) for a centering method in the context of linear models.”
The pseudo-marginal approach for efficient Monte Carlo computations (2009, methodology): “A solution suggested in [2, 6], which we will refer to as the standard RJ algo-rithm here, can be understood as being a simple variation on the algorithm above, where in the birth move the additional sampling of a new coeﬃcient from a distribution Q is required, resulting in the acceptance ratio 1…”
BAYESIAN ESTIMATION IN SINGLE-INDEX MODELS (2004, methodology): “Bayesian approaches have been successful with many other nonlinear regression models in recent years, e.g., additive semiparametric regression (Biller (2000)), semiparametric hazard function regression (McKeague and Tighiouart (2000)), nonparametric regression with measurement error (Berry, Carroll ”
Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions (2003, methodology): “See for example Green (2002) for results based on a Gaussian approximation and Ntzoufras et al. (2002) for applications to generalized linear models.”