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ORCIDJournal of Computational and Graphical Statistics, Volume 30
Volume 30, Number 1, January 2021
- Spencer Woody, Carlos M. Carvalho, Jared S. Murray:
Model Interpretation Through Lower-Dimensional Posterior Summarization. 144-161 - Chen Lin
, Kevin Y. X. Wang, Samuel Müller:
MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization. 125-132 - Myungjin Kim, Li Wang:
Generalized Spatially Varying Coefficient Models. 1-10 - Sevvandi Kandanaarachchi, Rob J. Hyndman:
Dimension Reduction for Outlier Detection Using DOBIN. 204-219 - Jeongyoun Ahn, Hee Cheol Chung, Yongho Jeon:
Trace Ratio Optimization for High-Dimensional Multi-Class Discrimination. 192-203 - Minh Pham, Anh Ninh, Hoang Le, Yufeng Liu:
An Efficient Algorithm for Minimizing Multi Non-Smooth Component Functions. 162-170 - Boqian Zhang, Vinayak Rao:
Efficient Parameter Sampling for Markov Jump Processes. 25-42 - Jingxiang Chen, Quoc Tran-Dinh, Michael R. Kosorok, Yufeng Liu:
Identifying Heterogeneous Effect Using Latent Supervised Clustering With Adaptive Fusion. 43-54 - JCGS Editorial Collaborators. i-iii
- Nathaniel E. Helwig:
Spectrally Sparse Nonparametric Regression via Elastic Net Regularized Smoothers. 182-191 - Yuan Feng, Luo Xiao, Eric C. Chi:
Sparse Single Index Models for Multivariate Responses. 115-124 - Chao Cheng, Rui Wang, Heping Zhang:
Surrogate Residuals for Discrete Choice Models. 67-77 - Stanislav Nagy, Jirí Dvorák:
Illumination Depth. 78-90 - Andrew J. Holbrook, Philippe Lemey, Guy Baele, Simon Dellicour, Dirk Brockmann, Andrew Rambaut, Marc A. Suchard:
Massive Parallelization Boosts Big Bayesian Multidimensional Scaling. 11-24 - George Karabatsos:
Fast Search and Estimation of Bayesian Nonparametric Mixture Models Using a Classification Annealing EM Algorithm. 236-247 - Ruiyan Luo, Xin Qi:
Functional Regression for Densely Observed Data With Novel Regularization. 220-235 - Zeda Li, Yuexiao Dong:
Model-Free Variable Selection With Matrix-Valued Predictors. 171-181 - Christian Rohrbeck, Jonathan A. Tawn:
Bayesian Spatial Clustering of Extremal Behavior for Hydrological Variables. 91-105 - Marcio Valk, Gabriela Bettella Cybis:
U-Statistical Inference for Hierarchical Clustering. 133-143 - Richard G. Everitt, Paulina A. Rowinska:
Delayed Acceptance ABC-SMC. 55-66 - Tao Zhang, Yang Ning, David Ruppert:
Optimal Sampling for Generalized Linear Models Under Measurement Constraints. 106-114
Volume 30, Number 2, April 2021
- Lewis J. Rendell, Adam M. Johansen, Anthony Lee, Nick Whiteley:
Global Consensus Monte Carlo. 249-259 - Ingmar Schuster, Ilja Klebanov:
Markov Chain Importance Sampling - A Highly Efficient Estimator for MCMC. 260-268 - Jaewoo Park, Murali Haran:
Reduced-Dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models. 269-283 - Jianchang Hu, Yazhen Wang:
Quantum Annealing via Path-Integral Monte Carlo With Data Augmentation. 284-296 - Qi Wang, Vinayak Rao, Yee Whye Teh:
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions. 297-311 - Philippe Gagnon, Arnaud Doucet:
Nonreversible Jump Algorithms for Bayesian Nested Model Selection. 312-323 - Nathan Robertson, James M. Flegal, Dootika Vats, Galin L. Jones:
Assessing and Visualizing Simultaneous Simulation Error. 324-334 - Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms. 335-348 - Amanda Lenzi, Stefano Castruccio, Håvard Rue, Marc G. Genton:
Improving Bayesian Local Spatial Models in Large Datasets. 349-359 - Priyanga Dilini Talagala, Rob J. Hyndman, Kate Smith-Miles:
Anomaly Detection in High-Dimensional Data. 360-374 - Mitchell Krock, William Kleiber, Stephen Becker:
Nonstationary Modeling With Sparsity for Spatial Data via the Basis Graphical Lasso. 375-389 - Daniel K. Sewell:
Model-Based Edge Clustering. 390-405 - Neil G. Marchant, Andee Kaplan, Daniel N. Elazar, Benjamin I. P. Rubinstein, Rebecca C. Steorts:
d-blink: Distributed End-to-End Bayesian Entity Resolution. 406-421 - Congyuan Yang, Carey E. Priebe, Youngser Park, David J. Marchette:
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering. 422-441 - Mengxi Yi, David E. Tyler:
Shrinking the Covariance Matrix Using Convex Penalties on the Matrix-Log Transformation. 442-451 - Jean-François Bégin, Mathieu Boudreault:
Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters. 452-466 - Nadja Klein, David J. Nott, Michael Stanley Smith:
Marginally Calibrated Deep Distributional Regression. 467-483 - Yao Chen, Qingyi Gao, Faming Liang, Xiao Wang:
Nonlinear Variable Selection via Deep Neural Networks. 484-492 - Indrayudh Ghosal, Giles Hooker:
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate. 493-502 - Rina Friedberg, Julie Tibshirani, Susan Athey, Stefan Wager:
Local Linear Forests. 503-517
Volume 30, Number 3, July 2021
- Youyi Fong, Jun Xu:
Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods. 519-529 - Pascaline Descloux, Sylvain Sardy:
Model Selection With Lasso-Zero: Adding Straw to the Haystack to Better Find Needles. 530-543 - Peng Zheng, Ryan Barber, Reed J. D. Sorensen, Christopher J. L. Murray, Aleksandr Y. Aravkin:
Trimmed Constrained Mixed Effects Models: Formulations and Algorithms. 544-556 - Ye Fan, Nan Lin, Xianjun Yin:
Penalized Quantile Regression for Distributed Big Data Using the Slack Variable Representation. 557-565 - Ana Kenney, Francesca Chiaromonte, Giovanni Felici:
MIP-BOOST: Efficient and Effective L0 Feature Selection for Linear Regression. 566-577 - Angelos Alexopoulos, Leonardo Bottolo:
Bayesian Variable Selection for Gaussian Copula Regression Models. 578-593 - Sanvesh Srivastava, Yixiang Xu:
Distributed Bayesian Inference in Linear Mixed-Effects Models. 594-611 - Aaron J. Molstad, Guangwei Weng, Charles R. Doss, Adam J. Rothman:
An Explicit Mean-Covariance Parameterization for Multivariate Response Linear Regression. 612-621 - Michael Jauch, Peter D. Hoff, David B. Dunson:
Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion. 622-631 - Rui Jin, Aixin Tan:
Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors. 632-646 - Marius Hofert, Avinash Prasad, Mu Zhu:
Quasi-Random Sampling for Multivariate Distributions via Generative Neural Networks. 647-670 - Tomasz Cakala, Blazej Miasojedow, Wojciech Niemiro:
Particle MCMC With Poisson Resampling: Parallelization and Continuous Time Models. 671-684 - Eric S. Kawaguchi, Jenny I. Shen, Marc A. Suchard, Gang Li:
Scalable Algorithms for Large Competing Risks Data. 685-693 - Cheng Meng, Rui Xie, Abhyuday Mandal, Xinlian Zhang, Wenxuan Zhong, Ping Ma:
LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model. 694-708 - Alessandra Menafoglio, Davide Pigoli, Piercesare Secchi:
Kriging Riemannian Data via Random Domain Decompositions. 709-727 - You-Lin Chen, Mladen Kolar, Ruey S. Tsay:
Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees. 728-744 - Clément Chevalier, Olivia Martius, David Ginsbourger:
Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling. 745-755 - Dorcas Ofori-Boateng, Yulia R. Gel, Ivor Cribben:
Nonparametric Anomaly Detection on Time Series of Graphs. 756-767 - Malte Londschien, Solt Kovács, Peter Bühlmann:
Change-Point Detection for Graphical Models in the Presence of Missing Values. 768-779 - Erjia Cui, Ciprian M. Crainiceanu, Andrew Leroux:
Additive Functional Cox Model. 780-793 - Zeda Li, Ori Rosen, Fabio Ferrarelli, Robert T. Krafty:
Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series. 794-807 - Zifeng Zhao, Chun Yip Yau:
Alternating Pruned Dynamic Programming for Multiple Epidemic Change-Point Estimation. 808-821
Volume 30, Number 4, October 2021
- Bradley S. Price, Aaron J. Molstad, Ben Sherwood:
Estimating Multiple Precision Matrices With Cluster Fusion Regularization. 823-834 - Mark A. van de Wiel, Mirrelijn M. van Nee, Armin Rauschenberger:
Fast Cross-validation for Multi-penalty High-dimensional Ridge Regression. 835-847 - Ting Yang, Zhiqiang Tan:
Hierarchical Total Variations and Doubly Penalized ANOVA Modeling for Multivariate Nonparametric Regression. 848-862 - Nelson Antunes, Shankar Bhamidi, Tianjian Guo, Vladas Pipiras, Bang Wang:
Sampling Based Estimation of In-Degree Distribution for Directed Complex Networks. 863-876 - Matias Quiroz, Minh-Ngoc Tran, Mattias Villani, Robert Kohn, Khue-Dung Dang:
The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC. 877-888 - Jonathan R. Bradley:
An Approach to Incorporate Subsampling Into a Generic Bayesian Hierarchical Model. 889-905 - Kjartan Kloster Osmundsen, Tore Selland Kleppe, Roman Liesenfeld:
Importance Sampling-Based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models. 906-919 - Haiying Wang, Dixin Zhang, Hua Liang, David Ruppert:
Iterative Likelihood: A Unified Inference Tool. 920-933 - Randy C. S. Lai, Jan Hannig, Thomas C. M. Lee:
Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference. 934-945 - Bingxin Zhou, Junbin Gao, Minh-Ngoc Tran, Richard Gerlach:
Manifold Optimization-Assisted Gaussian Variational Approximation. 946-957 - David T. Frazier, Christopher C. Drovandi:
Robust Approximate Bayesian Inference With Synthetic Likelihood. 958-976 - Xuejun Yu, David J. Nott, Minh-Ngoc Tran, Nadja Klein:
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. 977-990 - Martin Slawski, Guoqing Diao, Emanuel Ben-David:
A Pseudo-Likelihood Approach to Linear Regression With Partially Shuffled Data. 991-1003 - Xuening Zhu, Feng Li, Hansheng Wang:
Least-Square Approximation for a Distributed System. 1004-1018 - Lu Yang:
Assessment of Regression Models With Discrete Outcomes Using Quasi-Empirical Residual Distribution Functions. 1019-1035 - Yanyan Zeng, Hongyu Zhao, Tao Wang:
Model-Based Microbiome Data Ordination: A Variational Approximation Approach. 1036-1048 - Thomas Whitaker, Boris Beranger, Scott A. Sisson:
Logistic Regression Models for Aggregated Data. 1049-1067 - Arkaprava Roy, Brian J. Reich, Joseph Guinness, Russell T. Shinohara, Ana-Maria Staicu:
Spatial Shrinkage Via the Product Independent Gaussian Process Prior. 1068-1080 - Hsin-Cheng Huang, Noel Cressie, Andrew Zammit-Mangion, Guowen Huang:
False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data. 1081-1094 - Marcin Jurek, Matthias Katzfuss:
Multi-Resolution Filters for Massive Spatio-Temporal Data. 1095-1110 - Jesús Arroyo, Daniel L. Sussman, Carey E. Priebe, Vince Lyzinski:
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks. 1111-1123 - Danielle Sass, Bo Li, Brian J. Reich:
Flexible and Fast Spatial Return Level Estimation Via a Spatially Fused Penalty. 1124-1142 - Nan-Jung Hsu, Hsin-Cheng Huang, Ruey S. Tsay:
Matrix Autoregressive Spatio-Temporal Models. 1143-1155 - Thomas Rusch, Patrick Mair, Kurt Hornik:
Cluster Optimized Proximity Scaling. 1156-1167 - Natalia da Silva, Dianne Cook, Eun-Kyung Lee:
A Projection Pursuit Forest Algorithm for Supervised Classification. 1168-1180 - Torsten Hothorn, Achim Zeileis:
Predictive Distribution Modeling Using Transformation Forests. 1181-1196 - Luis E. Nieto-Barajas, Gabriel Núñez-Antonio:
Projected Pólya Tree. 1197-1208 - Jing Wu, Ming-Hui Chen, Elizabeth D. Schifano, Jun Yan:
Online Updating of Survival Analysis. 1209-1223 - Nathan Wycoff, Mickaël Binois, Stefan M. Wild:
Sequential Learning of Active Subspaces. 1224-1237 - Sang Jun Moon, Jong-June Jeon, Jason Sang Hun Lee, Yongdai Kim:
Learning Multiple Quantiles With Neural Networks. 1238-1248 - Grace Yoon, Christian L. Müller, Irina Gaynanova:
Fast Computation of Latent Correlations. 1249-1256
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