Variational Bridge Regression

Artin Armagan
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:17-24, 2009.

Abstract

Here we obtain approximate Bayes inferences through variational methods when an exponential power family type prior is specified for the regression coefficients to mimic the characteristics of the Bridge regression. We accomplish this through hierarchical modeling of such priors. Although the mixing distribution is not explicitly stated for scale normal mixtures, we obtain the required moments only to attain the variational distributions for the regression coefficients. By choosing specific values of hyper-parameters (tuning parameters) present in the model, we can mimic the model selection performance of best subset selection in sparse underlying settings. The fundamental difference between MAP, maximum a posteriori, estimation and the proposed method is that, here we can obtain approximate inferences besides a point estimator. We also empirically analyze the frequentist properties of the estimator obtained. Results suggest that the proposed method yields an estimator that performs significantly better in sparse underlying setups than the existing state-of-the-art procedures in both n>p and p>n scenarios.

Cite this Paper

BibTeX
@InProceedings{pmlr-v5-armagan09a, title = {Variational Bridge Regression}, author = {Armagan, Artin}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {17--24}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/armagan09a/armagan09a.pdf}, url = {https://proceedings.mlr.press/v5/armagan09a.html}, abstract = {Here we obtain approximate Bayes inferences through variational methods when an exponential power family type prior is specified for the regression coefficients to mimic the characteristics of the Bridge regression. We accomplish this through hierarchical modeling of such priors. Although the mixing distribution is not explicitly stated for scale normal mixtures, we obtain the required moments only to attain the variational distributions for the regression coefficients. By choosing specific values of hyper-parameters (tuning parameters) present in the model, we can mimic the model selection performance of best subset selection in sparse underlying settings. The fundamental difference between MAP, maximum a posteriori, estimation and the proposed method is that, here we can obtain approximate inferences besides a point estimator. We also empirically analyze the frequentist properties of the estimator obtained. Results suggest that the proposed method yields an estimator that performs significantly better in sparse underlying setups than the existing state-of-the-art procedures in both n>p and p>n scenarios.} }
Endnote
%0 Conference Paper %T Variational Bridge Regression %A Artin Armagan %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-armagan09a %I PMLR %P 17--24 %U https://proceedings.mlr.press/v5/armagan09a.html %V 5 %X Here we obtain approximate Bayes inferences through variational methods when an exponential power family type prior is specified for the regression coefficients to mimic the characteristics of the Bridge regression. We accomplish this through hierarchical modeling of such priors. Although the mixing distribution is not explicitly stated for scale normal mixtures, we obtain the required moments only to attain the variational distributions for the regression coefficients. By choosing specific values of hyper-parameters (tuning parameters) present in the model, we can mimic the model selection performance of best subset selection in sparse underlying settings. The fundamental difference between MAP, maximum a posteriori, estimation and the proposed method is that, here we can obtain approximate inferences besides a point estimator. We also empirically analyze the frequentist properties of the estimator obtained. Results suggest that the proposed method yields an estimator that performs significantly better in sparse underlying setups than the existing state-of-the-art procedures in both n>p and p>n scenarios.
RIS
TY - CPAPER TI - Variational Bridge Regression AU - Artin Armagan BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-armagan09a PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 17 EP - 24 L1 - http://proceedings.mlr.press/v5/armagan09a/armagan09a.pdf UR - https://proceedings.mlr.press/v5/armagan09a.html AB - Here we obtain approximate Bayes inferences through variational methods when an exponential power family type prior is specified for the regression coefficients to mimic the characteristics of the Bridge regression. We accomplish this through hierarchical modeling of such priors. Although the mixing distribution is not explicitly stated for scale normal mixtures, we obtain the required moments only to attain the variational distributions for the regression coefficients. By choosing specific values of hyper-parameters (tuning parameters) present in the model, we can mimic the model selection performance of best subset selection in sparse underlying settings. The fundamental difference between MAP, maximum a posteriori, estimation and the proposed method is that, here we can obtain approximate inferences besides a point estimator. We also empirically analyze the frequentist properties of the estimator obtained. Results suggest that the proposed method yields an estimator that performs significantly better in sparse underlying setups than the existing state-of-the-art procedures in both n>p and p>n scenarios. ER -
APA
Armagan, A.. (2009). Variational Bridge Regression. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:17-24 Available from https://proceedings.mlr.press/v5/armagan09a.html.

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