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Independence-Encouraging Subsampling for Nonparametric Additive Models 非参数加法模型的独立鼓励子采样
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-03-01 DOI: 10.1080/10618600.2024.2326136
Yi Zhang, Lin Wang, Xiaoke Zhang, HaiYing Wang
{"title":"Independence-Encouraging Subsampling for Nonparametric Additive Models","authors":"Yi Zhang, Lin Wang, Xiaoke Zhang, HaiYing Wang","doi":"10.1080/10618600.2024.2326136","DOIUrl":"https://doi.org/10.1080/10618600.2024.2326136","url":null,"abstract":"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...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"1 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140000947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A multi-attribute evaluation of genotype-environment experiments using biplots and joint plots graphics 利用双图和联合图对基因型-环境实验进行多属性评估
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-29 DOI: 10.1080/10618600.2024.2325445
Jhessica Leticia Kirch, Acácia Mecejana Diniz Souza Spitti, Alisson Fernando Chiorato, Carlos Tadeu dos Santos Dias, César Gonçalves de Lima
{"title":"A multi-attribute evaluation of genotype-environment experiments using biplots and joint plots graphics","authors":"Jhessica Leticia Kirch, Acácia Mecejana Diniz Souza Spitti, Alisson Fernando Chiorato, Carlos Tadeu dos Santos Dias, César Gonçalves de Lima","doi":"10.1080/10618600.2024.2325445","DOIUrl":"https://doi.org/10.1080/10618600.2024.2325445","url":null,"abstract":"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...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"8 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140000936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A penalized criterion for selecting the number of clusters for K-medians 为 K 媒介选择聚类数量的惩罚性标准
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-29 DOI: 10.1080/10618600.2024.2325458
Antoine Godichon-Baggioni, Sobihan Surendran
{"title":"A penalized criterion for selecting the number of clusters for K-medians","authors":"Antoine Godichon-Baggioni, Sobihan Surendran","doi":"10.1080/10618600.2024.2325458","DOIUrl":"https://doi.org/10.1080/10618600.2024.2325458","url":null,"abstract":"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...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"30 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140000945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Journal of Computational and Graphical Statistics 2023 Associate Editors 计算与图形统计学杂志》2023 年副主编
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-26 DOI: 10.1080/10618600.2024.2319476
{"title":"The Journal of Computational and Graphical Statistics 2023 Associate Editors","authors":"","doi":"10.1080/10618600.2024.2319476","DOIUrl":"https://doi.org/10.1080/10618600.2024.2319476","url":null,"abstract":"Published in Journal of Computational and Graphical Statistics (Vol. 33, No. 1, 2024)","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"12 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices 用于零膨胀数据矩阵协同聚类的深度动态潜块模型
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-23 DOI: 10.1080/10618600.2024.2319162
Giulia Marchello, Marco Corneli, Charles Bouveyron
{"title":"A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices","authors":"Giulia Marchello, Marco Corneli, Charles Bouveyron","doi":"10.1080/10618600.2024.2319162","DOIUrl":"https://doi.org/10.1080/10618600.2024.2319162","url":null,"abstract":"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...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"1 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139988227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hammock plots: visualizing categorical and numerical variables 吊床图：分类和数字变量的可视化
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-22 DOI: 10.1080/10618600.2024.2322561
Matthias Schonlau
{"title":"Hammock plots: visualizing categorical and numerical variables","authors":"Matthias Schonlau","doi":"10.1080/10618600.2024.2322561","DOIUrl":"https://doi.org/10.1080/10618600.2024.2322561","url":null,"abstract":"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...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"34 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139994287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An interpretable neural network-based non-proportional odds model for ordinal regression 基于神经网络的可解释非比例赔率序数回归模型
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-22 DOI: 10.1080/10618600.2024.2321208
Akifumi Okuno, Kazuharu Harada
{"title":"An interpretable neural network-based non-proportional odds model for ordinal regression","authors":"Akifumi Okuno, Kazuharu Harada","doi":"10.1080/10618600.2024.2321208","DOIUrl":"https://doi.org/10.1080/10618600.2024.2321208","url":null,"abstract":"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-...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"47 18 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139988331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality 预测高维函数时间序列：国家以下各年龄段死亡率的应用
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-20 DOI: 10.1080/10618600.2024.2319166
Cristian F. Jiménez-Varón, Ying Sun, Han Lin Shang
{"title":"Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality","authors":"Cristian F. Jiménez-Varón, Ying Sun, Han Lin Shang","doi":"10.1080/10618600.2024.2319166","DOIUrl":"https://doi.org/10.1080/10618600.2024.2319166","url":null,"abstract":"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...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"290 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nonparametric Additive Models for Billion Observations 十亿观测数据的非参数加法模型
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-15 DOI: 10.1080/10618600.2024.2319684
Mengyu Li, Jingyi Zhang, Cheng Meng
{"title":"Nonparametric Additive Models for Billion Observations","authors":"Mengyu Li, Jingyi Zhang, Cheng Meng","doi":"10.1080/10618600.2024.2319684","DOIUrl":"https://doi.org/10.1080/10618600.2024.2319684","url":null,"abstract":"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 ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"23 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139739579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness* 异质缺失情况下复杂调查抽样中的混合矩阵完成 *
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-02-14 DOI: 10.1080/10618600.2024.2319154
Xiaojun Mao, Hengfang Wang, Zhonglei Wang, Shu Yang
{"title":"Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness*","authors":"Xiaojun Mao, Hengfang Wang, Zhonglei Wang, Shu Yang","doi":"10.1080/10618600.2024.2319154","DOIUrl":"https://doi.org/10.1080/10618600.2024.2319154","url":null,"abstract":"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...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"11 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139739531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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