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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...
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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 ...
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Testing Model Specification in Approximate Bayesian Computation Using Asymptotic Properties J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-11 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 ...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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Correction J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-31
Published in Journal of Computational and Graphical Statistics (Ahead of Print, 2024)
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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 ...
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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...
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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...
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Performance is not enough: the story told by a Rashomon quartet J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-05-14 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...
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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 ...
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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...
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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 ...
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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 ...
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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...
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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...
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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)...
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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...
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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...
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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...
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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...
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On inference for modularity statistics in structured networks J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-04-01 Anirban Mitra, Konasale Prasad, Joshua Cape
This paper revisits the classical concept of network modularity and its spectral relaxations used throughout graph data analysis. We formulate and study several modularity statistic variants for wh...
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High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-03-13 Namjoon Suh, Li-Hsiang Lin, Xiaoming Huo
We consider a low-rank matrix estimation problem when the data is assumed to be generated from the multivariate linear regression model. To induce the low-rank coefficient matrix, we employ the wei...
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An exact game-theoretic variable importance index for generalized additive models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-03-13 Amir Khorrami Chokami, Giovanni Rabitti
Generalized Additive Models (GAMs) are widely used in statistics. In this work, we aim to tackle the challenge of identifying the most influential variables in GAMs. To accomplish this, we introduc...
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Variational Bayesian Neural Networks via Resolution of Singularities J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-03-14 Susan Wei, Edmund Lau
In this work, we advocate for the importance of singular learning theory (SLT) as it pertains to the theory and practice of variational inference in Bayesian neural networks (BNNs). To begin, we la...
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Semiparametric Probit Regression Model with General Interval-Censored Failure Time Data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-03-12 Yi Deng, Shuwei Li, Liuquan Sun, Xinyuan Song
Interval-censored data frequently arise in various biomedical areas involving periodical follow-ups where the failure or event time of interest cannot be observed exactly but is only known to fall ...
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Generative Quantile Regression with Variability Penalty J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-03-08 Shijie Wang, Minsuk Shin, Ray Bai
Quantile regression and conditional density estimation can reveal structure that is missed by mean regression, such as multimodality and skewness. In this paper, we introduce a deep learning genera...
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Independence-Encouraging Subsampling for Nonparametric Additive Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-03-01 Yi Zhang, Lin Wang, Xiaoke Zhang, HaiYing Wang
The additive model is a popular nonparametric regression method due to its ability to retain modeling flexibility while avoiding the curse of dimensionality. The backfitting algorithm is an intuiti...
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A penalized criterion for selecting the number of clusters for K-medians J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-29 Antoine Godichon-Baggioni, Sobihan Surendran
Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i...
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A multi-attribute evaluation of genotype-environment experiments using biplots and joint plots graphics J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-29 Jhessica Leticia Kirch, Acácia Mecejana Diniz Souza Spitti, Alisson Fernando Chiorato, Carlos Tadeu dos Santos Dias, César Gonçalves de Lima
In plant breeding studies, some of objectives are to study the interaction between genotype and environment (GEI), evaluating genotypic stability and adaptability. The additive model with multiplic...
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A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-23 Giulia Marchello, Marco Corneli, Charles Bouveyron
The simultaneous clustering of observations and features of data sets (known as co-clustering) has recently emerged as a central machine learning application to summarize massive data sets. However...
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The Journal of Computational and Graphical Statistics 2023 Associate Editors J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-26
Published in Journal of Computational and Graphical Statistics (Vol. 33, No. 1, 2024)
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Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-20 Cristian F. Jiménez-Varón, Ying Sun, Han Lin Shang
We study the modeling and forecasting of high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. We introduce a decomposition of the HDF...
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Hammock plots: visualizing categorical and numerical variables J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-22 Matthias Schonlau
I discuss the hammock plot for visualizing categorical or mixed categorical/numeric data. Hammock plots can be viewed as a generalization of parallel coordinate plots where the lines are replaced b...
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An interpretable neural network-based non-proportional odds model for ordinal regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-22 Akifumi Okuno, Kazuharu Harada
This study proposes an interpretable neural network-based non-proportional odds model (N3POM) for ordinal regression. N3POM is different from conventional approaches to ordinal regression with non-...
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Functional linear model with prior information of subjects’ network J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-14 Xiaochen Zhang, Qingzhao Zhang, Kuangnan Fang
In many modern applications, data samples are interconnected by a network, and network information is a crucial factor in forecasting. However, existing network data analysis methods, which are des...
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Structured variational approximations with skew normal decomposable graphical models and implicit copulas J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-14 Robert Salomone, Xuejun Yu, David J. Nott, Robert Kohn
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally ...
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Nonparametric Additive Models for Billion Observations J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-15 Mengyu Li, Jingyi Zhang, Cheng Meng
The nonparametric additive model (NAM) is a widely used nonparametric regression method. Nevertheless, due to the high computational burden, classic statistical techniques for fitting NAMs are not ...
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MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-14 Zhiwei Wang, Fa Zhang, Cong Zheng, Xianghong Hu, Mingxuan Cai, Can Yang
In various practical situations, matrix factorization methods suffer from poor data quality, such as high data sparsity and low signal-to-noise ratio (SNR). Here, we consider a matrix factorization...
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Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness* J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-14 Xiaojun Mao, Hengfang Wang, Zhonglei Wang, Shu Yang
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by...
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Statistical inference in circular structural model and fitting circles to noisy data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-02-12 A. Donner, A. Goldenshluger
It is well known that commonly used algorithms for circle fitting perform poorly when sampling distribution of the points is not symmetric with respect to the circle center, e.g., when the points a...
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Bayesian Hyperbolic Multidimensional Scaling J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-01-26 Bolun Liu, Shane Lubold, Adrian E. Raftery, Tyler H. McCormick
Multidimensional scaling (MDS) is a widely used approach to representing high-dimensional, dependent data. MDS works by assigning each observation a location on a low-dimensional geometric manifold...
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Renewable Quantile Regression with Heterogeneous Streaming Datasets J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-01-23 Xuerong Chen, Senlin Yuan
The renewable statistical inference has received much attention since the advent of streaming data collection techniques. However, most existing online updating methods are developed based on a hom...
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Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-01-23 Hengrui Luo, Younghyun Cho, James W. Demmel, Xiaoye S. Li, Yang Liu
This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical...
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Communication-Efficient Nonparametric Quantile Regression via Random Features J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-01-23 Caixing Wang, Tao Li, Xinyi Zhang, Xingdong Feng, Xin He
This paper introduces a refined algorithm designed for distributed nonparametric quantile regression in a reproducing kernel Hilbert space (RKHS). Unlike existing nonparametric approaches that prim...
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A framework for leveraging machine learning tools to estimate personalized survival curves J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-01-10 Charles J. Wolock, Peter B. Gilbert, Noah Simon, Marco Carone
The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of scientific interest ...
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Functional Mixed Membership Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-01-10 Nicholas Marco, Damla Şentürk, Shafali Jeste, Charlotte DiStefano, Abigail Dickinson, Donatello Telesca
Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this paper, we propose a Bayesian ...