TY - GEN
T1 - Bayesian Adaptive Selection Under Prior Ignorance
AU - Basu, Tathagata
AU - Troffaes, Matthias C. M.
AU - Einbeck, Jochen
N1 - Basu, T., Troffaes, M.C. ., Einbeck, J. (2021). Bayesian Adaptive Selection Under Prior Ignorance. In: Vasile, M., Quagliarella, D. (eds) Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. UQOP 2020. Space Technology Proceedings, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-030-80542-5_22
PY - 2021/7/16
Y1 - 2021/7/16
N2 - Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian models have been proposed for variable selection. However, a convincing robust Bayesian approach is yet to be investigated. Here in this work, we investigate sensitivity analysis over a simplex of probability measures. We sample from this simplex to get an inclusion probability of each variable. The sensitivity analysis gives us a set of posteriors instead of a single posterior. This set of posteriors gives us a behaviour of the model parameters with respect to different prior elicitations resulting in robust inferential conclusions.
AB - Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian models have been proposed for variable selection. However, a convincing robust Bayesian approach is yet to be investigated. Here in this work, we investigate sensitivity analysis over a simplex of probability measures. We sample from this simplex to get an inclusion probability of each variable. The sensitivity analysis gives us a set of posteriors instead of a single posterior. This set of posteriors gives us a behaviour of the model parameters with respect to different prior elicitations resulting in robust inferential conclusions.
KW - Bayesian variable selection
KW - statistics
KW - high dimensional statistics
KW - sensitivity analysis
KW - variable selection
KW - imprecise probability
U2 - 10.1007/978-3-030-80542-5_22
DO - 10.1007/978-3-030-80542-5_22
M3 - Conference contribution book
SN - 9783030805418
VL - 8
T3 - Space Technology Proceedings
SP - 365
EP - 378
BT - International Conference on Uncertainty Quantification Optimisation 2020
PB - Springer
CY - Cham
T2 - UQOP: International Conference on Uncertainty Quantification & Optimisation
Y2 - 16 November 2020 through 19 November 2020
ER -