Beyond Prediction: A Framework for Inference With Variational Approximations in Mixture Models
Beyond Prediction: A Framework for Inference With Variational Approximations in Mixture Models
复制标题
超越预测:混合模型中变分近似的推理框架
DOI:
10.1080/10618600.2019.1609977
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发表时间:
2019
影响因子:
2.4
通讯作者:
McCormick, T. H.
中科院分区:
文献类型:
--
作者:
Westling, T.;McCormick, T. H.
Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise frequently in many settings in the health, social, and biological sciences. Variational inference in a frequentist context works by approximating intractable conditional distributions with a tractable family and optimizing the resulting lower bound on the log-likelihood. The variational objective function is typically less computationally intensive to optimize than the true likelihood, enabling scientists to fit rich models even with extremely large datasets. Despite widespread use, little is known about the general theoretical properties of estimators arising from variational approximations to the log-likelihood, which hinders their use in inferential statistics. In this article, we connect such estimators to profileM-estimation, which enables us to provide regularity conditions for consistency and asymptotic normality of variational estimators. Our theory also motivates three methodological improvements to variational inference: estimation of the asymptotic model-robust covariance matrix, a one-step correction that improves estimator efficiency, and an empirical assessment of consistency. We evaluate the proposed results using simulation studies and data on marijuana use from the National Longitudinal Study of Youth. Supplementary materials for this article are available online.
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影响因子:
3.3
作者:
J. K. Pritchard;Matthew Stephens;Peter Donnelly
通讯作者:
J. K. Pritchard;Matthew Stephens;Peter Donnelly
影响因子:
32.8
作者:
Wainwright, Martin J.;Jordan, Michael I.
通讯作者:
Jordan, Michael I.
DOI:
--
发表时间:
2009
期刊:
Communications in Statisteics-Theory and Methods Vol.38No.1
影响因子:
--
作者:
中山武憲;小川英治;Y. Omori and R. A. Johnson
通讯作者:
Y. Omori and R. A. Johnson
影响因子:
4.5
作者:
Bickel, Peter;Choi, David;Zhang, Hai
通讯作者:
Zhang, Hai
DOI:
10.1111/1467-9868.00350
发表时间:
2002
期刊:
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
影响因子:
--
作者:
P. Hall;Keith Humphreys;D. Titterington
通讯作者:
D. Titterington