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High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization J. Comput. Graph. Stat. (IF 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 2.4) 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 ...
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Optimization for Calibration of Survey Weights under a Large Number of Conflicting Constraints J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-09 Matthew R. Williams, Terrance D. Savitsky
In the analysis of survey data, sampling weights are needed for consistent estimation of the population; however, weights are typically modified through a process termed “calibration” to increase t...
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Supervised Stratified Subsampling for Predictive Analytics J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-09 Ming-Chung Chang
Predictive analytics involves the use of statistical models to make predictions; however, the power of these techniques is hindered by ever-increasing quantities of data. The richness and sheer vol...
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Competing Risk Modeling with Bivariate Varying Coefficients to Understand the Dynamic Impact of COVID-19 J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-09 Wenbo Wu, John D. Kalbfleisch, Jeremy M. G. Taylor, Jian Kang, Kevin He
The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. A preliminary analysis of ...
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Double Probability Integral Transform Residuals for Regression Models with Discrete Outcomes J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-08 Lu Yang
The assessment of regression models with discrete outcomes is challenging and has many fundamental issues. With discrete outcomes, standard regression model assessment tools such as Pearson and dev...
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Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-05 Jiahe Lin, Huitian Lei, George Michailidis
Structural discovery amongst a set of variables is of interest in both static and dynamic settings. In the presence of lead-lag dependencies in the data, the dynamics of the system can be represent...
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Functional Labeled Optimal Partitioning J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-05 Jacob M. Kaufman, Alyssa J. Stenberg, Toby D. Hocking
Peak detection is a problem in sequential data analysis that involves differentiating regions with higher counts (peaks) from regions with lower counts (background noise). It is crucial to correctl...
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Accelerated structured matrix factorization J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-03 Lorenzo Schiavon, Bernardo Nipoti, Antonio Canale
Matrix factorization exploits the idea that, in complex high-dimensional data, the actual signal typically lies in lower-dimensional structures. These lower dimensional objects provide useful insig...
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Properties of Test Statistics for Nonparametric Cointegrating Regression Functions Based on Subsamples J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-03 Sepideh Mosaferi, Mark S. Kaiser, Daniel J. Nordman
Nonparametric cointegrating regression models have been extensively used in financial markets, stock prices, heavy traffic, climate data sets, and energy markets. Models with parametric regression ...
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Adaptive wavelet domain principal component analysis for nonstationary time series J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-02 Marina I. Knight, Matthew A. Nunes, Jessica K. Hargreaves
High-dimensional multivariate nonstationary time series, i.e. data whose second order properties vary over time, are common in many scientific and industrial applications. In this article we propos...
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A Bayesian Collocation Integral Method for Parameter Estimation in Ordinary Differential Equations J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-05 Mingwei Xu, Samuel W.K. Wong, Peijun Sang
Abstract–Inferring the parameters of ordinary differential equations (ODEs) from noisy observations is an important problem in many scientific fields. Currently, most parameter estimation methods t...
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Group-Orthogonal Subsampling for Hierarchical Data Based on Linear Mixed Models J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-01-03 Jiaqing Zhu, Lin Wang, Fasheng Sun
Hierarchical data analysis is crucial in various fields for making discoveries. The linear mixed model is often used for training hierarchical data, but its parameter estimation is computationally ...
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Times Square sampling: an adaptive algorithm for free energy estimation J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-12-12 Cristian Predescu, Michael Snarski, Avi Robinson-Mosher, Duluxan Sritharan, Tamas Szalay, David E. Shaw
Estimating free energy differences, an important problem in computational drug discovery and in a wide range of other application areas, commonly involves a computationally intensive process of sam...
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Generative Multi-purpose Sampler for Weighted M-estimation J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-12-08 Minsuk Shin, Shijie Wang, Jun S. Liu
To overcome computational bottlenecks of various data perturbation procedures such as the bootstrap and cross validations, we propose the Generative Multi-purpose Sampler (GMS), which directly cons...
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Copula Graphical Models for Heterogeneous Mixed Data J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-12-06 Sjoerd Hermes, Joost vanHeerwaarden, Pariya Behrouzi
This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of sampl...
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Smooth multi-period forecasting with application to prediction of COVID-19 cases J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-12-04 Elena Tuzhilina, Trevor J. Hastie, Daniel J. McDonald, J. Kenneth Tay, Robert Tibshirani
Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider the problem of mul...
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A Geometric Algorithm for Contrastive Principal Component Analysis in High Dimension J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-12-01 Rung-Sheng Lu, Shao-Hsuan Wang, Su-Yun Huang
Principal component analysis (PCA) has been widely used in exploratory data analysis. Contrastive PCA (Abid et al., 2018), a generalized method of PCA, is a new tool used to capture features of a t...
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Model-based Smoothing with Integrated Wiener Processes and Overlapping Splines J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-29 Ziang Zhang, Alex Stringer, Patrick Brown, Jamie Stafford
In many applications that involve the inference of an unknown smooth function, the inference of its derivatives is also important. To make joint inferences of the function and its derivatives, a cl...
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Extrapolated cross-validation for randomized ensembles J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-27 Jin-Hong Du, Pratik Patil, Kathryn Roeder, Arun Kumar Kuchibhotla
Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters...
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Statement of Retraction: Detecting Anomalous Time Series by GAMLSS-Akaike-Weights-Scoring J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-27
Published in Journal of Computational and Graphical Statistics (Vol. 33, No. 1, 2024)
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Vecchia Likelihood Approximation for Accurate and Fast Inference with Intractable Spatial Max-Stable Models J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-21 Raphaël Huser, Michael L. Stein, Peng Zhong
Max-stable processes are the most popular models for high-impact spatial extreme events, as they arise as the only possible limits of spatially-indexed block maxima. However, likelihood inference f...
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An adaptive neural network regression method for structure identification J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-21 Jae-Kyung Shin, Kwan-Young Bak, Ja-Yong Koo
This paper reports a study on a flexible neural network regression method within the functional analysis of variance framework that aims to adapt to the underlying structure of the target function....
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A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-15 Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais
Sampling-based algorithms are classical approaches to perform Bayesian inference in inverse problems. They provide estimators with the associated credibility intervals to quantify the uncertainty o...
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Joint and Individual Component Regression J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-16 Peiyao Wang, Haodong Wang, Quefeng Li, Dinggang Shen, Yufeng Liu
Abstract–Multi-group data, which include the same set of variables on separate groups of samples, are commonly seen in practice. Such data structure consists of data from multiple groups and can be...
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Fast matrix-free methods for model-based personalized synthetic MR imaging J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-15 Subrata Pal, Somak Dutta, Ranjan Maitra
Synthetic Magnetic Resonance (MR) imaging predicts images at new design parameter settings from a few observed MR scans. Model-based methods, that use both the physical and statistical properties u...
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Fast Bayesian Record Linkage for Streaming Data Contexts J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-15 Ian Taylor, Andee Kaplan, Brenda Betancourt
Record linkage is the task of combining records from multiple files which refer to overlapping sets of entities when there is no unique identifying field. In streaming record linkage, files arrive ...
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Improved Pathwise Coordinate Descent for Power Penalties J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-15 Maryclare Griffin
Pathwise coordinate descent algorithms have been used to compute entire solution paths for lasso and other penalized regression problems quickly with great success. They improve upon cold start alg...
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Iteratively Reweighted Least Squares Method for Estimating Polyserial and Polychoric Correlation Coefficients J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-07 Peng Zhang, Ben Liu, Jingjing Pan
An iteratively reweighted least squares (IRLS) method is proposed for the estimation of polyserial and polychoric correlation coefficients in this article. It calculates the slopes in a series of w...
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Discovering Active Subspaces for High-Dimensional Computer Models J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-03 Kellin Rumsey, Devin Francom, Scott Vander Wiel
Dimension reduction techniques have long been an important topic in statistics, and active subspaces (AS) have received much attention this past decade in the computer experiments literature. The m...
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A fast solution to the lasso problem with equality constraints J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-03 Lam Tran, Gen Li, Lan Luo, Hui Jiang
The equality-constrained lasso problem augments the standard lasso by imposing additional structure on regression coefficients. Despite the broad utilities of the equality-constrained lasso, existi...
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Spatially smoothed robust covariance estimation for local outlier detection J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-03 Patricia Puchhammer, Peter Filzmoser
Most multivariate outlier detection procedures ignore the spatial dependency of observations, which is present in many real data sets from various application areas. This paper introduces a new out...
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Metaheuristic Solutions to Order-of-Addition Design Problems J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-03 Zack Stokes, Weng Kee Wong, Hongquan Xu
There is increasing recognition that the order of administration of drugs in drug combination studies can markedly affect the outcome. Similarly, manufactured products are often sequentially produc...
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Measure of Strength of Evidence for Visually Observed Differences between Subpopulations J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-11-02 Xi Yang, Jan Hannig, Katherine A. Hoadley, Iain Carmichael, J.S. Marron
For measuring the strength of visually-observed subpopulation differences, the Population Difference Criterion is proposed to assess the statistical significance of visually observed subpopulation ...
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Deeply-Learned Generalized Linear Models with Missing Data J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-10-26 David K. Lim, Naim U. Rashid, Junier B. Oliva, Joseph G. Ibrahim
Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to various supervised learning problems. However, the greater prev...
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Recurrent event analysis in the presence of real-time high frequency data via random subsampling J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-10-26 Walter Dempsey
Digital monitoring studies collect real-time high frequency data via mobile sensors in the subjects’ natural environment. This data can be used to model the impact of changes in physiology on recur...
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A Unified Algorithm for Penalized Convolution Smoothed Quantile Regression J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2023-10-27 Rebeka Man, Xiaoou Pan, Kean Ming Tan, Wen-Xin Zhou
Penalized quantile regression (QR) is widely used for studying the relationship between a response variable and a set of predictors under data heterogeneity in high-dimensional settings. Compared t...