Jun S. Liu

发表

A tutorial on R package dslice

Chao Ye, Xuegong Zhang, Bo Jiang, 2014 .

Bayesian Aggregation of Rank Data with Covariates and Heterogeneous Rankers

Xinran Li, Jun S. Liu, Dingdong Yi, 2016, 1607.06051.

Additional file 19

Limsoon Wong, Vladimir B. Bajic, Rajesh Chowdhary, 2010 .

Sequential Monte Carlo methods for dynamic systems

Jun S. Liu, Rong Chen, 1997 .

Sequential importance sampling for nonparametric Bayes models: The next generation

Jun S. Liu, S. MacEachern, M. Clyde, 1999 .

Sequential Imputations and Bayesian Missing Data Problems

Jun S. Liu, W. Wong, A. Kong, 1994 .

Mixture Kalman ®lters

Jun S. Liu, Rong Chen, 2000 .

Monte Carlo strategies in scientific computing

Jun S. Liu, Jun S. Liu, 2001 .

Blind Deconvolution via Sequential Imputations

Jun S. Liu, Rong Chen, 1995 .

Predictive updating methods with application to Bayesian classification

Jun S. Liu, Rong Chen, 1996 .

Nonparametric hierarchical Bayes via sequential imputations

Jun S. Liu, 1996 .

The Multiple-Try Method and Local Optimization in Metropolis Sampling

Jun S. Liu, W. Wong, F. Liang, 2000 .

Metropolized independent sampling with comparisons to rejection sampling and importance sampling

Jun S. Liu, Jun S. Liu, 1996, Stat. Comput..

Rejection Control and Sequential Importance Sampling

Jun S. Liu, Rong Chen, W. Wong, 1998 .

STATISTICAL APPLICATIONS OF THE POISSON-BINOMIAL AND CONDITIONAL BERNOULLI DISTRIBUTIONS

Jun S. Liu, Sean X. Chen, 1997 .

The Wang-Landau algorithm in general state spaces: Applications and convergence analysis

Jun S. Liu, Y. Atchadé, 2010 .

MetaGen: reference-free learning with multiple metagenomic samples

Jun S. Liu, Wenxuan Zhong, Xin Xing, 2017, Genome Biology.

Generalised Gibbs sampler and multigrid Monte Carlo for Bayesian computation

Jun S. Liu, C. Sabatti, 2000 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Monte Carlo Bayesian Signal Processing for Wireless Communications

Rong Chen, Xiaodong Wang, Jun S. Liu, 2002, J. VLSI Signal Process..

Implementation of Estimating Function-Based Inference Procedures With Markov Chain Monte Carlo Samplers

Jun S. Liu, L. J. Wei, L. Tian, 2007 .

Variable selection for general index models via sliced inverse regression

Bo Jiang, Jun S. Liu, Jun S. Liu, 2013, 1304.4056.

On consistency and sparsity for sliced inverse regression in high dimensions

Jun S. Liu, Zhigen Zhao, Jun S. Liu, 2015, 1507.03895.

Sparse Sliced Inverse Regression via Lasso

Jun S. Liu, Zhigen Zhao, Q. Lin, 2016, Journal of the American Statistical Association.

Simulated Sintering: Markov Chain Monte Carlo With Spaces of Varying Dimensions

Jun S. Liu, Chiara Sabatti, Jun S. Liu, 2007 .

A Theory for Dynamic Weighting in Monte Carlo Computation

Faming Liang, Jun S. Liu, Jun S. Liu, 2001 .

Bayesian biclustering of gene expression data

Jun S Liu, Jun S. Liu, J. Gu, 2008, BMC Genomics.

On learning strategies for evolutionary Monte Carlo

Jun S. Liu, Jun S. Liu, Gopi Goswami, 2007, Stat. Comput..

Evolutionary Monte Carlo Methods for Clustering

Jun S. Liu, Jun S. Liu, Wing Hung Wong, 2007 .

Jun S. Liu, Xiaodan Fan, 2009 .

The Wang-Landau Algorithm for Monte Carlo computation in general state spaces

Jun S. Liu, Y. Atchadé, 2005 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

On the optimality of sliced inverse regression in high dimensions

Jun S. Liu, Q. Lin, Xinran Li, 2017, The Annals of Statistics.

Minimax Nonparametric Two-sample Test.

Jun S. Liu, Ping Ma, Wenxuan Zhong, 2019, 1911.02171.

Nonparametric K-Sample Tests via Dynamic Slicing

Bo Jiang, Jun S. Liu, Jun S. Liu, 2015 .

An Adaptive Exchange Algorithm for Sampling From Distributions With Intractable Normalizing Constants

Faming Liang, Jun S. Liu, Qifan Song, 2016 .

A Bayesian Model for Aggregating Rank Data with Covariates

Xinran Li, Jun S. Liu, Dingdong Yi, 2016 .

Discussion of the paper by Kou , Zhou and Wong

Jun S. Liu, Y. Atchadé, 2005 .

Statistical Methods in Bioinformatics

Jun S. Liu, B. Jiang, 2013 .

NONPARAMETRIC HIERARCHICAL BAYES VIA SEQUENTIAL IMPUTATIONS 1 By

Jun S. Liu, Jun S. Liu, 2022 .

Controlling False Discovery Rate Using Gaussian Mirrors

Jun S. Liu, Zhigen Zhao, Xin Xing, 2019, Journal of the American Statistical Association.

A new sequential importance sampling method and its application to the two-dimensional hydrophobic-hydrophilic model

Jun S. Liu, Jun S. Liu, Junni L. Zhang, 2002 .

Stopping‐time resampling for sequential Monte Carlo methods

Yuguo Chen, Jun S. Liu, Junyi Xie, 2005 .

Peskun's theorem and a modified discrete-state Gibbs sampler

Jun S. Liu, 1996 .

Parameter Expansion for Data Augmentation

Jun S. Liu, Y. Wu, 1999 .

ST ] 4 M ay 2 01 8 GLOBAL TESTING UNDER SPARSE ALTERNATIVES FOR SINGLE INDEX MODELS By

Jun S. Liu, Zhigen Zhao, Q. Lin, 2018 .

Power of FDR Control Methods: The Impact of Ranking Algorithm, Tampered Design, and Symmetric Statistic

Jun S. Liu, Z. Ke, Yucong Ma, 2020, 2010.08132.

A Scale-free Approach for False Discovery Rate Control in Generalized Linear Models

Jun S. Liu, Xin Xing, Chenguang Dai, 2020, Journal of the American Statistical Association.

SLICED INVERSE REGRESSION WITH VARIABLE SELECTION AND INTERACTION DETECTION

Jun S. Liu, B. Jiang, 2013 .

The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem

Jun S. Liu, 2012 .

Varying Coefficient Model via Adaptive Spline Fitting

Jun S. Liu, B. Jiang, Xufei Wang, 2022, 2201.10063.

Selective Bayesian Forest Classifier: Simultaneous Variable Selection and Classification

Jun S. Liu, Viktoriya Krakovna, Jiong Du, 2015 .

LOOKAHEAD AND PILOTING STRATEGIES FOR VARIABLE SELECTION

Jun S. Liu, Rong Chen, Junni L. Zhang, 2007 .

Statistical inference and Monte Carlo algorithms

Jun S. Liu, Xiao-Li Meng, L. Wasserman, 1996 .

Testing Model Utility for Single Index Models Under High Dimension

Zhigen Zhao, Jun S. Liu, Jun S. Liu, 2021 .

Supplement to “Bayesian Partition Models for Identifying Expression Quantitative Trait Loci”

Jun S. Liu, B. Jiang, 2015 .

Signed Support Recovery for Single Index Models in High-Dimensions

Jun S. Liu, Matey Neykov, Jun S. Liu, 2015, 1511.02270.

Monte Carlo EM with importance reweighting and its applications in random effects models 1 1 This wo

Jun S. Liu, F. Quintana, G. D. Pino, 1999 .

Dynamic Weighting In Markov Chain Monte Carlo

Faming Liang, Jun S. Liu, Jun S. Liu, 2007 .

Lookahead Strategies for Sequential Monte Carlo this access benefits you. Your story matters

Jun S. Liu, 2013 .

Lookahead Strategies for Sequential Monte Carlo

Jun S. Liu, 2013 .

Scalable Uncertainty Quantification via GenerativeBootstrap Sampler

Minsuk Shin, Lu Wang, Jun S Liu, 2020 .

Bayesian Inference for Assessing Effects of Email Marketing Campaigns

Jun S. Liu, Kate J. Li, Jiexing Wu, 2018 .

Bayesian bi-clustering methods with applications in computational biology

Jun S. Liu, Y. Li, Jiexing Wu, 2020, The Annals of Applied Statistics.

Model selection principles in misspecified models

Jun S. Liu, Jinchi Lv, Jun S. Liu, 2010, 1005.5483.

Generative Multiple-purpose Sampler for Weighted M-estimation

Jun S. Liu, Minsuk Shin, Shijie Wang, 2020, 2006.00767.

Bayesian nonparametric tests via sliced inverse modeling

Jun S. Liu, B. Jiang, Chao Ye, 2014, 1411.3070.

Mixture Kalman filters

Jun S. Liu, Jun S. Liu, Rong Chen, 2000 .

Bayesian Partition Models for Identifying Expression Quantitative Trait Loci

Bo Jiang, Jun S. Liu, Jun S. Liu, 2015, Journal of the American Statistical Association.

Sequential Monte Carlo Methods for Statistical Analysis of Tables

Yuguo Chen, Persi Diaconis, Jun S. Liu, 2005 .

Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers

Martha L. Bulyk, Bo Jiang, Jun S. Liu, 2013, Bioinform..

The EM Algorithm and the Rise of Computational Biology

Jun S. Liu, Xiaodan Fan, Yuan Yuan, 2010, 1104.2180.

MetaGen: reference-free learning with multiple metagenomic samples

Jun S. Liu, Wenxuan Zhong, Xin Xing, 2017, Genome Biology.

The properties of the cross-match estimate and split sampling

Augustine Kong, Jun S. Liu, Jun S. Liu, 1997 .

False Discovery Rate Control via Data Splitting

Jun S. Liu, Xin Xing, Chenguang Dai, 2020, Journal of the American Statistical Association.

BAYESIAN META-ANALYSIS FOR IDENTIFYING PERIODICALLY EXPRESSED GENES IN FISSION YEAST CELL CYCLE

Saumyadipta Pyne, Jun S. Liu, Xiaodan Fan, 2010, 1011.2104.

Statistical Learning and Modeling of TF-DNA Binding

Bo Jiang, Jun S. Liu, Jun S. Liu, 2011, Handbook of Statistical Bioinformatics.

Bayesian Analysis of Rank Data with Covariates and Heterogeneous Rankers

Jun S. Liu, Jun S. Liu, Dingdong Yi, 2016 .

An Extended Mallows Model for Ranked Data Aggregation

Han Li, Jun S. Liu, Minxuan Xu, 2020, Journal of the American Statistical Association.

A Theoretical Framework for Sequential Importance Sampling with Resampling

Rong Chen, Jun S. Liu, Jun S. Liu, 2001, Sequential Monte Carlo Methods in Practice.

Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks

Pengyu Hong, Jun S. Liu, Xin Xing, 2020, ICLR.

Monte Carlo Approximation of Bayes Factors via Mixing With Surrogate Distributions

Jun S. Liu, Chenguang Dai, 2019, Journal of the American Statistical Association.

Metropolis Jumping Rules

Jun S. Liu, Chenguang Dai, Jun S. Liu, 2020 .

The Wang-Landau Algorithm as Stochastic Optimization and its Acceleration

Jun S. Liu, Chenguang Dai, Jun S. Liu, 2019, Physical review. E.

Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics

Xin Lu, Arndt Asperger, Xuegong Zhang, 2004, Proteomics.

SLICED INVERSE REGRESSION WITH VARIABLE SELECTION AND INTERACTION DETECTION

Jun S. Liu, B. Jiang, Jun S. Liu, 2013 .

Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests

Jun S. Liu, Jiong Du, Jun S. Liu, 2015, 1506.02371.

Additivity tests for nonlinear autoregression

Jun S. Liu, Ruey S. Tsay, Jun S. Liu, 1995 .

Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering

Jun S. Liu, Rong Chen, Xiaodong Wang, 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

Measurement error models: from nonparametric methods to deep neural networks pdf

Zhirui Hu, Jun S Liu, Zheng Tracy Ke, 2020, Statistical Science.

Sequential importance sampling for

Jun S. Liu, Steven N. MacEachern, Merlise A. Clyde, 1999 .

Metropolized independent sampling with comparisons to rejection sampling and importance sampling

Jun S. Liu, 1996, Stat. Comput..

Multi-Response Heteroscedastic Gaussian Process Models and Their Inference

Jun S. Liu, Taehee Lee, 2023, ArXiv.

Jun S. Liu, L. J. Wei, L. Tian, 2007 .

Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application

Faming Liang, Jason Cong, Wing Hung Wong, 2000, Proceedings 2000. Design Automation Conference. (IEEE Cat. No.00CH37106).

Generalized R-squared for Detecting Non-independence

Jun S. Liu, B. Jiang, Xufei Wang, 2016 .

Simulated Sintering : Markov Chain Monte Carlo With Spaces of Varying Dimensions

Jun S. Liu, A. F. Smith, J. Berger, 1998 .

Global testing under the sparse alternatives for single index models

Jun S. Liu, Zhigen Zhao, Q. Lin, 2018, 1805.01820.

Supplementary to ”Minimax Nonparametric Multi-sample Test Under Smoothing”

Jun S. Liu, Ping Ma, Wenxuan Zhong, 2023 .

Monotone Cubic B-Splines with a Neural-Network Generator

Jun S. Liu, Xiaodan Fan, Lijun Wang, 2023, 2307.01748.

Generative Multi-purpose Sampler for Weighted M-estimation

Jun S. Liu, Minsuk Shin, Shijie Wang, 2020, Journal of Computational and Graphical Statistics.

Sparse Sliced Inverse Regression for High Dimensional Data

Jun S. Liu, Zhigen Zhao, Jun S. Liu, 2016 .

Lookahead Strategies for Sequential Monte Carlo

Rong Chen, Jun S. Liu, Ming Lin, 2013, 1302.5206.

Monotone Cubic B-Splines with a Neural-Network Generator

Jun S. Liu, Xiaodan Fan, Lijun Wang, 2023, 2307.01748.

Relaxed simulated tempering for VLSI floorplan designs

Faming Liang, Jason Cong, Wing Hung Wong, 1999, Proceedings of the ASP-DAC '99 Asia and South Pacific Design Automation Conference 1999 (Cat. No.99EX198).