样式: 排序: IF: - GO 导出 标记为已读
-
Wasserstein-Kaplan-Meier Survival Regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-09-17 Yidong Zhou, Hans-Georg Müller
Survival analysis plays a pivotal role in medical research, offering valuable insights into the timing of events such as survival time. One common challenge in survival analysis is the necessity to...
-
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-09-12 Pratik Nag, Yiping Hong, Sameh Abdulah, Ghulam A. Qadir, Marc G. Genton, Ying Sun
Spatial processes observed in various fields, such as climate and environmental science, often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a Gaussian process with...
-
Bayesian nowcasting with Laplacian-P-splines J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-09-12 Bryan Sumalinab, Oswaldo Gressani, Niel Hens, Christel Faes
During an epidemic, the daily number of reported infected cases, deaths or hospitalizations is often lower than the actual number due to reporting delays. Nowcasting aims to estimate the cases that...
-
Optimal Subsampling for Functional Quasi-Mode Regression with Big Data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-09-12 Tao Wang
We propose investigating optimal subsampling for functional regression with massive datasets based on the mode value, which is referred to as functional quasi-mode regression, to reduce data volume...
-
Efficient convex PCA with applications to Wasserstein GPCA and ranked data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-09-12 Steven Campbell, Ting-Kam Leonard Wong
Convex PCA, which was introduced in Bigot et al. (2017), modifies Euclidean PCA by restricting the data and the principal components to lie in a given convex subset of a Hilbert space. This setting...
-
Co-factor analysis of citation networks J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-27 Alex Hayes, Karl Rohe
One compelling use of citation networks is to characterize papers by their relationships to the surrounding literature. We propose a method to characterize papers by embedding them into two distinc...
-
Fast Bayesian Inference for Spatial Mean-Parameterized Conway–Maxwell–Poisson Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-21 Bokgyeong Kang, John Hughes, Murali Haran
Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are ...
-
Degrees of Freedom: Search Cost and Self-consistency J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-08 Lijun Wang, Hongyu Zhao, Xiaodan Fan
Model degrees of freedom ( df ) is a fundamental concept in statistics because it quantifies the flexibility of a fitting procedure and is indispensable in model selection. To investigate the gap b...
-
Beyond time-homogeneity for continuous-time multistate Markov models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-08 Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen, Frederic Bois, Vo Hong Thanh
Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describ...
-
Scalable Estimation for Structured Additive Distributional Regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-08 Nikolaus Umlauf, Johannes Seiler, Mattias Wetscher, Thorsten Simon, Stefan Lang, Nadja Klein
Obtaining probabilistic models is of high relevance in many recent applications. However, estimation of such distributional models with very large datasets remains a difficult task. In particular, ...
-
Using rejection sampling probability of acceptance as a measure of independence J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-06 Markku Kuismin
This paper proposes a new association statistic for determining whether random variables are statistically independent. The proposed association statistic can also be used to examine the strength o...
-
Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-05 Yizhen Xu, Joseph Hogan, Michael Daniels, Rami Kantor, Ann Mwangi
The multinomial probit (MNP) (Imai and van Dyk, 2005) framework is based on a multivariate Gaussian latent structure, allowing for natural extensions to multilevel modeling. Unlike multinomial logi...
-
Blocked Gibbs sampler for hierarchical Dirichlet processes J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-05 Snigdha Das, Yabo Niu, Yang Ni, Bani K. Mallick, Debdeep Pati
Posterior computation in hierarchical Dirichlet process (HDP) mixture models is an active area of research in nonparametric Bayes inference of grouped data. Existing literature almost exclusively f...
-
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-15 Jiajun Liang, Qian Zhang, Wei Deng, Qifan Song, Guang Lin
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertain...
-
Using early rejection Markov chain Monte Carlo and Gaussian processes to accelerate ABC methods J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-15 Xuefei Cao, Shijia Wang, Yongdao Zhou
Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets problems with intractable or unavailable likelihood functions. It uses synthetic data drawn from the ...
-
Computational methods for fast Bayesian model assessment via calibrated posterior p-values J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-11 Sally Paganin, Perry de Valpine
Posterior predictive p-values (ppps) have become popular tools for Bayesian model assessment, being general-purpose and easy to use. However, interpretation can be difficult because their distribut...
-
Stochastic Block Smooth Graphon Model J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Benjamin Sischka, Göran Kauermann
In this paper, we propose combining the stochastic blockmodel and the smooth graphon model, two of the most prominent modeling approaches in statistical network analysis. Stochastic blockmodels are...
-
A Tidy Framework and Infrastructure to Systematically Assemble Spatio-temporal Indexes from Multivariate Data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez
Indexes are useful for summarizing multivariate information into single metrics for monitoring, communicating, and decision-making. While most work has focused on defining new indexes for specific ...
-
Continuous-time multivariate analysis J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Biplab Paul, Philip T. Reiss, Erjia Cui, Noemi Foà
The starting point for much of multivariate analysis (MVA) is an n × p data matrix whose n rows represent observations and whose p columns represent variables. Some multivariate data sets, however,...
-
Fast Computer Model Calibration using Annealed and Transformed Variational Inference J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Dongkyu Derek Cho, Won Chang, Jaewoo Park
Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models...
-
Functional Time Series Analysis and Visualization Based on Records J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Israel Martínez-Hernández, Marc G. Genton
In many phenomena, data are collected on a large scale and at different frequencies. In this context, functional data analysis (FDA) has become an important statistical methodology for analyzing an...
-
Global inference and test for eigensystems of imaging data over complicated domains J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Leheng Cai, Qirui Hu
A nonparametric approach for analyzing eigensystems of image data over a complex domain is novelly developed. The proposed estimators, which are based on bivariate splines, have both oracle efficie...
-
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee
Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivariate and non-Eucli...
-
Dynamic prediction using landmark historical functional Cox regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-02 Andrew Leroux, Ciprian Crainiceanu
Dynamic prediction of survival data in the presence of time-varying covariates is an area of active research. Two common analytic approaches for this type of data are joint modeling of the longitud...
-
On the Wasserstein Median of Probability Measures J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Kisung You, Dennis Shung, Mauro Giuffrè
The primary choice to summarize a finite collection of random objects is by using measures of central tendency, such as mean and median. In the field of optimal transport, the Wasserstein barycente...
-
Bayesian L12 regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Xiongwen Ke, Yanan Fan
It is well known that Bridge regression Knight et al. (2000) enjoys superior theoretical properties when compared to traditional LASSO. However, the current latent variable representation of its Ba...
-
Testing Model Specification in Approximate Bayesian Computation Using Asymptotic Properties J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-24 Andrés Ramírez-Hassan, David T. Frazier
We present a novel procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation (ABC). Unlike previous procedures, our proposal is ...
-
A distribution-free method for change point detection in non-sparse high dimensional data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-12 Reza Drikvandi, Reza Modarres
We propose a distribution-free distance-based method for high dimensional change points that can address challenging situations when the sample size is very small compared to the dimension as in th...
-
Principal variables analysis for non-Gaussian data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-13 Dylan Clark-Boucher, Jeffrey W. Miller
Principal variables analysis (PVA) is a technique for selecting a subset of variables that capture as much of the information in a dataset as possible. Existing approaches for PVA are based on the ...
-
Interval-censored linear quantile regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-11 Taehwa Choi, Seohyeon Park, Hunyong Cho, Sangbum Choi
Censored quantile regression has emerged as a prominent alternative to classical Cox’s proportional hazards model or accelerated failure time model in both theoretical and applied statistics. While...
-
Generating Independent Replicates Directly from the Posterior Distribution for a Class of Spatial Hierarchical Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-11 Jonathan R. Bradley, Madelyn Clinch
Markov chain Monte Carlo (MCMC) allows one to generate dependent replicates from a posterior distribution for effectively any Bayesian hierarchical model. However, MCMC can produce a significant co...
-
Ultra-efficient MCMC for Bayesian longitudinal functional data analysis J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-07 Thomas Y. Sun, Daniel R. Kowal
Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bay...
-
Distance-based clustering of functional data with derivative principal component analysis J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-11 Ping Yu, Gongmin Shi, Chunjie Wang, Xinyuan Song
Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is clustering, which identifies subgroups based on the shapes of meas...
-
Performance Is Not Enough: The Story Told by a Rashomon Quartet J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-07 Przemysław Biecek, Hubert Baniecki, Mateusz Krzyziński, Dianne Cook
The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure. However, what if the explanation provided by this model is completely diffe...
-
Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-05 Fabio Centofanti, Mia Hubert, Biagio Palumbo, Peter J. Rousseeuw
Multivariate Singular Spectrum Analysis (MSSA) is a powerful and widely used nonparametric method for multivariate time series, which allows the analysis of complex temporal data from diverse field...
-
Parsimonious Tensor Dimension Reduction J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-04 Xin Xing, Peng Zeng, Youhui Ye, Wenxuan Zhong
Abstract–Tensor data is emerging in many scientific applications, such as multi-tissue transcriptomics. In such cases, the covariates for each individual are no longer a vector. To apply traditiona...
-
Kernel Angle Dependence Measures in Metric Spaces J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-03 Yilin Zhang, Songshan Yang
Measuring and testing dependence between data in separable metric spaces is of great importance in modern statistics. Most existing work relied on the distance between random variables, which inevi...
-
Correction J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-31
Published in Journal of Computational and Graphical Statistics (Vol. 33, No. 3, 2024)
-
Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-31 Nathaniel E. Helwig
This paper proposes an adaptively bounded gradient descent (ABGD) algorithm for group elastic net penalized regression. Unlike previously proposed algorithms, the proposed algorithm adaptively boun...
-
Class-Distributed Learning for Multinomial Logistic Regression with High Dimensional Features and a Large Number of Classes J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-31 Shuyuan Wu, Jing Zhou, Ke Xu, Hansheng Wang
Estimating a high-dimensional multinomial logistic regression model with a larger number of categories is of fundamental importance but it presents two challenges. Computationally, it leads to heav...
-
Iterated Data Sharpening J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-31 Hanxiao Chen, W. John Braun, Xiaoping Shi
Data sharpening in kernel regression has been shown to be an effective method of reducing bias while having minimal effects on variance. Earlier efforts to iterate the data sharpening procedure hav...
-
Multiple-use calibration for all future values and exact two-sided simultaneous tolerance intervals in linear regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-28 Yang Han, Lingjiao Wang, Wei Liu, Frank Bretz
Multiple-use calibration using regression is an important statistical tool. Confidence sets for the x-values associated with all future y-values should guarantee a key property, which can be satisf...
-
Dynamic Survival Prediction Using Sparse Longitudinal Images via Multi-Dimensional Functional Principal Component Analysis J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-23 Haolun Shi, Shu Jiang, Da Ma, Mirza Faisal Beg, Jiguo Cao
Our work is motivated by predicting the progression of Alzheimer’s disease (AD) based on a series of longitudinally observed brain scan images. Existing works on dynamic prediction for AD focus pri...
-
smashGP: Large-scale Spatial Modeling via Matrix-free Gaussian Processes J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-22 Lucas Erlandson, Ana María Estrada Gómez, Edmond Chow, Kamran Paynabar
Gaussian processes are essential for spatial data analysis. Not only do they allow the prediction of unknown values, but they also allow for uncertainty quantification. However, in the era of big d...
-
Nonparametric high-dimensional multi-sample tests based on graph theory J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-21 Xiaoping Shi
High-dimensional data pose unique challenges for data processing in an era of ever-increasing amounts of data availability. Graph theory can provide a structure of high-dimensional data. We introdu...
-
A Projection Approach to Local Regression with Variable-Dimension Covariates J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-20 Matthew J. Heiner, Garritt L. Page, Fernando Andrés Quintana
Incomplete covariate vectors are known to be problematic for estimation and inferences on model parameters, but their impact on prediction performance is less understood. We develop an imputation-f...
-
Nonparametric testing of the covariate significance for spatial point patterns under the presence of nuisance covariates J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-20 Jiří Dvořák, Tomáš Mrkvička
Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model ...
-
Fast calculation of Gaussian process multiple-fold cross-validation residuals and their covariances J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-17 David Ginsbourger, Cédric Schärer
We generalize fast Gaussian process leave-one-out formulae to multiple-fold cross-validation, highlighting in turn the covariance structure of cross-validation residuals in simple and universal kri...
-
Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-15 Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo, Roberta De Vito
Factors models are commonly used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Carlo (MCMC) methods...
-
Fast and Robust Low-Rank Learning over Networks: A Decentralized Matrix Quantile Regression Approach J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-09 Nan Qiao, Canyi Chen
Decentralized low-rank learning is an active research domain with extensive practical applications. A common approach to producing low-rank and robust estimations is to employ a combination of the ...
-
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-07 Evan Sidrow, Nancy Heckman, Alexandre Bouchard-Côté, Sarah M. E. Fortune, Andrew W. Trites, Marie Auger-Méthé
Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large data sets can be computationally demanding because m...
-
Universal inference meets random projections: a scalable test for log-concavity J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-25 Robin Dunn, Aditya Gangrade, Larry Wasserman, Aaditya Ramdas
Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concavity is motivated ...
-
A Plot is Worth a Thousand Tests: Assessing Residual Diagnostics with the Lineup Protocol J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-22 Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas
Regression experts consistently recommend plotting residuals for model diagnosis, despite the availability of many numerical hypothesis test procedures designed to use residuals to assess problems ...
-
Mapper–type algorithms for complex data and relations J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-22 Paweł Dłotko, Davide Gurnari, Radmila Sazdanovic
Mapper and Ball Mapper are Topological Data Analysis tools used for exploring high dimensional point clouds and visualizing scalar–valued functions on those point clouds. Inspired by open questions...
-
Group Selection and Shrinkage: Structured Sparsity for Semiparametric Additive Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-22 Ryan Thompson, Farshid Vahid
Sparse regression and classification estimators that respect group structures have application to an assortment of statistical and machine learning problems, from multitask learning to sparse addit...
-
Loss-Based Variational Bayes Prediction J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-16 David T. Frazier, Rubén Loaiza-Maya, Gael M. Martin, Bonsoo Koo
We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional (but parametric)...
-
Wavelet feature screening J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-15 Rodney Fonseca, Pedro Morettin, Aluísio Pinheiro
An initial screening of which covariates are relevant is a common practice in high-dimensional regression models. The classic feature screening selects only a subset of covariates correlated with t...
-
Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-12 Traymon E. Beavers, Ge Cheng, Yajie Duan, Javier Cabrera, Mariusz Lubomirski, Dhammika Amaratunga, Jeffrey E. Teigler
Big data, with N×P dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, suc...
-
Distributed Learning for Principal Eigenspaces without Moment Constraints J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-12 Yong He, Zichen Liu, Yalin Wang
Distributed Principal Component Analysis (PCA) has been studied to deal with the case when data are stored across multiple machines and communication cost or privacy concerns prohibit the computati...
-
Relative Entropy Gradient Sampler for Unnormalized Distribution J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-09 Xingdong Feng, Yuan Gao, Jian Huang, Yuling Jiao, Xu Liu
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms iteratively pushin...