Research output per year
Research output per year
- 30 results
Search results
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2023
Learning Topic Models: Identifiability and Finite-Sample Analysis
Chen, Y., He, S., Yang, Y. & Liang, F., 2023, In: Journal of the American Statistical Association. 118, 544, p. 2860-2875 16 p.Research output: Contribution to journal › Article › peer-review
Open Access -
2022
An Ensemble EM Algorithm for Bayesian Variable Selection
Wang, J., Ouyang, Y., Ji, Y. & Liang, F., Sep 2022, In: Bayesian Analysis. 17, 3, p. 879-900 22 p.Research output: Contribution to journal › Article › peer-review
Open Access -
BAYESIAN ESTIMATION OF GAUSSIAN CONDITIONAL RANDOM FIELDS
Gan, L., Narisetty, N. N. & Liang, F., Jan 2022, In: Statistica Sinica. 32, 1, p. 131-152 22 p.Research output: Contribution to journal › Article › peer-review
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Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models
Yin, W., Zhao, S. D. & Liang, F., Apr 2022, In: Lifetime Data Analysis. 28, 2, p. 282-318 37 p.Research output: Contribution to journal › Article › peer-review
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2021
Complementary dimension reduction
Cui, N., Hu, J. & Liang, F., Feb 2021, In: Statistical Analysis and Data Mining. 14, 1, p. 31-40 10 p.Research output: Contribution to journal › Article › peer-review
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GemBag: Group estimation of multiple bayesian graphical models
Yang, X., Gan, L., Narisetty, N. N. & Liang, F., 2021, In: Journal of Machine Learning Research. 22Research output: Contribution to journal › Article › peer-review
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2020
A Sparse Latent Class Model for Cognitive Diagnosis
Chen, Y., Culpepper, S. & Liang, F., Mar 1 2020, In: Psychometrika. 85, 1, p. 121-153 33 p.Research output: Contribution to journal › Article › peer-review
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2019
Bayesian Regularization for Graphical Models With Unequal Shrinkage
Gan, L., Narisetty, N. N. & Liang, F., Jul 3 2019, In: Journal of the American Statistical Association. 114, 527, p. 1218-1231 14 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Impact of Building Design Parameters on Daylighting Metrics Using an Analysis, Prediction, and Optimization Approach Based on Statistical Learning Technique
Lee, J., Boubekri, M. & Liang, F., Mar 10 2019, In: Sustainability. 11, 5, 21 p., 1474.Research output: Contribution to journal › Article › peer-review
Open Access -
2017
Bayesian calibration of groundwater models with input data uncertainty
Xu, T., Valocchi, A. J., Ye, M., Liang, F. & Lin, Y. F., Apr 1 2017, In: Water Resources Research. 53, 4, p. 3224-3245 22 p.Research output: Contribution to journal › Article › peer-review
Open Access -
hnRNP I regulates neonatal immune adaptation and prevents colitis and colorectal cancer
Jin, Z., Liang, F., Yang, J. & Mei, W., Mar 2017, In: PLoS genetics. 13, 3, e1006672.Research output: Contribution to journal › Article › peer-review
Open Access -
Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model
Xu, T., Valocchi, A. J., Ye, M. & Liang, F., May 1 2017, In: Water Resources Research. 53, 5, p. 4084-4105 22 p.Research output: Contribution to journal › Article › peer-review
Open Access -
2013
A graph-based consensus maximization approach for combining multiple supervised and unsupervised models
Gao, J., Liang, F., Fan, W., Sun, Y. & Han, J., 2013, In: IEEE Transactions on Knowledge and Data Engineering. 25, 1, p. 15-28 14 p., 6035707.Research output: Contribution to journal › Article › peer-review
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Kernel sliced inverse regression: Regularization and consistency
Wu, Q., Liang, F. & Mukherjee, S., 2013, In: Abstract and Applied Analysis. 2013, 540725.Research output: Contribution to journal › Article › peer-review
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2012
Comment on “Universality of Bayesian Prediction” by A. Sancetta
Liang, F., 2012, In: Bayesian Analysis.Research output: Contribution to journal › Article › peer-review
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From Minimax Shrinkage Estimation to Minimax Shrinkage Prediction
George, E. I., Liang, F. & Xu, X., Feb 2012, In: Statistical Science. 27, 1, p. 82-94 13 p.Research output: Contribution to journal › Article › peer-review
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Permutation test for incomplete paired data with application to cDNA microarray data
Yu, D., Lim, J., Liang, F., Kim, K., Kim, B. S. & Jang, W., Mar 1 2012, In: Computational Statistics and Data Analysis. 56, 3, p. 510-521 12 p.Research output: Contribution to journal › Article › peer-review
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Reno: Regularized non-parametric analysis of protein lysate array data
Li, B., Liang, F., Hu, J. & He, X., May 2012, In: Bioinformatics. 28, 9, p. 1223-1229 7 p., bts131.Research output: Contribution to journal › Article › peer-review
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2010
Asymptotic minimax risk of predictive density estimation for nonparametric regression
Xu, X. & Liang, F., 2010, In: Bernoulli. 16, 2Research output: Contribution to journal › Article › peer-review
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Asymptotic minimax risk of predictive density estimation for non-parametric regression
Xu, X. & Liang, F., May 2010, In: Bernoulli. 16, 2, p. 543-560 18 p.Research output: Contribution to journal › Article › peer-review
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Bayesian co-segmentation of multiple MR images
Liang, F. & Xu, J., 2010, In: Statistics and its Interface.Research output: Contribution to journal › Article › peer-review
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Localized sliced inverse regression
Wu, Q., Liang, F. & Mukherjee, S., Dec 2010, In: Journal of Computational and Graphical Statistics. 19, 4, p. 843-860 18 p.Research output: Contribution to journal › Article › peer-review
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2009
Bayesian function estimation using continuous wavelet dictionaries
Chu, J. H., Clyde, M. A. & Liang, F., Oct 2009, In: Statistica Sinica. 19, 4, p. 1419-1438 20 p.Research output: Contribution to journal › Article › peer-review
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On clustering fMRI using Potts and mixture regression models.
Xia, J., Liang, F. & Wang, Y. M., Dec 1 2009, In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. p. 4795-4798 4 p.Research output: Contribution to journal › Article › peer-review
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2008
Mixtures of g priors for Bayesian variable selection
Liang, F., Paulo, R., Molina, G., Clyde, M. A. & Berger, J. O., Mar 2008, In: Journal of the American Statistical Association. 103, 481, p. 410-423 14 p.Research output: Contribution to journal › Article › peer-review
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2007
Characterizing the function space for bayesian kernel models
Pillai, N. S., Wu, Q., Liang, F., Mukherjee, S. & Wolpert, R. L., Aug 2007, In: Journal of Machine Learning Research. 8, p. 1769-1797 29 p.Research output: Contribution to journal › Article › peer-review
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The use of unlabeled data in predictive modeling
Liang, F., Mukherjee, S. & West, M., May 2007, In: Statistical Science. 22, 2, p. 189-205 17 p.Research output: Contribution to journal › Article › peer-review
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2006
Improved minimax predictive densities under Kullback-Leibler loss
George, E. I., Liang, F. & Xu, X., Feb 2006, In: Annals of Statistics. 34, 1, p. 78-91 14 p.Research output: Contribution to journal › Article › peer-review
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2004
Exact minimax strategies for predictive density estimation, data compression, and model selection
Liang, F. & Barron, A., Nov 2004, In: IEEE Transactions on Information Theory. 50, 11, p. 2708-2726 19 p.Research output: Contribution to journal › Article › peer-review
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2001
On relationship inference using gamete identity by descent data
Zhao, H. & Liang, F., 2001, In: Journal of Computational Biology. 8, 2, p. 191-200 10 p.Research output: Contribution to journal › Article › peer-review
Open Access