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The impact of misclassifications and outliers on imputation methods 误分类和异常值对估算方法的影响
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-03-05 DOI: 10.1080/02664763.2024.2325969
M. Templ, Markus Ulmer
{"title":"The impact of misclassifications and outliers on imputation methods","authors":"M. Templ, Markus Ulmer","doi":"10.1080/02664763.2024.2325969","DOIUrl":"https://doi.org/10.1080/02664763.2024.2325969","url":null,"abstract":"Many imputation methods have been developed over the years and tested mostly under ideal settings. Surprisingly, there is no detailed research on how imputation methods perform when the idealized a...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Factor model for ordinal categorical data with latent factors explained by auxiliary variables applied to the major depression inventory 适用于重度抑郁量表的带有辅助变量解释的潜在因子的序数分类数据因子模型
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-03-04 DOI: 10.1080/02664763.2024.2321913
Alana Tavares Viana, Kelly Cristina Mota Gonçalves, Marina Silva Paez
{"title":"Factor model for ordinal categorical data with latent factors explained by auxiliary variables applied to the major depression inventory","authors":"Alana Tavares Viana, Kelly Cristina Mota Gonçalves, Marina Silva Paez","doi":"10.1080/02664763.2024.2321913","DOIUrl":"https://doi.org/10.1080/02664763.2024.2321913","url":null,"abstract":"In behavioral and social research, questionnaires are an important assessment tool, through which individuals can be categorized according to how they classify themselves in respect to a personal t...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"232 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 new factor analysis model for factors obeying a Gamma distribution 服从伽马分布因子的新因子分析模型
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-28 DOI: 10.1080/02664763.2024.2317299
Guoqiong Zhou, Wenjiang Jiang, Shixun Lin
{"title":"A new factor analysis model for factors obeying a Gamma distribution","authors":"Guoqiong Zhou, Wenjiang Jiang, Shixun Lin","doi":"10.1080/02664763.2024.2317299","DOIUrl":"https://doi.org/10.1080/02664763.2024.2317299","url":null,"abstract":"The traditional factor analysis model assumes that the factors obey a normal distribution, which is not appropriate in fields whose data are nonnegative. For this kind of problem, we construct a mo...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140010899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 robust likelihood approach to inference for paired multiple binary endpoints data 推断成对多二进制端点数据的稳健似然法
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-27 DOI: 10.1080/02664763.2024.2321904
Tsung-Shan Tsou, Wei-Cheng Hsiao
{"title":"A robust likelihood approach to inference for paired multiple binary endpoints data","authors":"Tsung-Shan Tsou, Wei-Cheng Hsiao","doi":"10.1080/02664763.2024.2321904","DOIUrl":"https://doi.org/10.1080/02664763.2024.2321904","url":null,"abstract":"We introduce a robust likelihood approach to inference for paired multiple binary endpoints data. One can easily implement the methodology without dealing with the model that incorporates a large n...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"51 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140018666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 novel M-Lognormal–Burr regression model with varying threshold for modeling heavy-tailed claim severity data 用于重尾理赔严重程度数据建模的具有不同阈值的新型 M-Lognormal-Burr 回归模型
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-26 DOI: 10.1080/02664763.2024.2319232
Girish Aradhye, Deepesh Bhati, George Tzougas
{"title":"A novel M-Lognormal–Burr regression model with varying threshold for modeling heavy-tailed claim severity data","authors":"Girish Aradhye, Deepesh Bhati, George Tzougas","doi":"10.1080/02664763.2024.2319232","DOIUrl":"https://doi.org/10.1080/02664763.2024.2319232","url":null,"abstract":"In this study, we explore the potential of composite probability distributions in effectively modeling claim severity data, which encompasses a spectrum of losses, ranging from minor to substantial...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"25 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140010902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Bayesian parametric estimation based on left-truncated competing risks data under bivariate Clayton copula models 基于双变量克莱顿共轭模型下左截断竞争风险数据的贝叶斯参数估计
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-22 DOI: 10.1080/02664763.2024.2315458
Hirofumi Michimae, Takeshi Emura, Atsushi Miyamoto, Kazuma Kishi
{"title":"Bayesian parametric estimation based on left-truncated competing risks data under bivariate Clayton copula models","authors":"Hirofumi Michimae, Takeshi Emura, Atsushi Miyamoto, Kazuma Kishi","doi":"10.1080/02664763.2024.2315458","DOIUrl":"https://doi.org/10.1080/02664763.2024.2315458","url":null,"abstract":"In observational/field studies, competing risks and left-truncation may co-exist, yielding ‘left-truncated competing risks’ settings. Under the assumption of independent competing risks, parametric...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"199 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS 利用 GLM-PO2PLS 对结果变量和综合全息数据集进行联合建模
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-21 DOI: 10.1080/02664763.2024.2313458
Zhujie Gu, Hae-Won Uh, Jeanine Houwing-Duistermaat, Said el Bouhaddani
{"title":"Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS","authors":"Zhujie Gu, Hae-Won Uh, Jeanine Houwing-Duistermaat, Said el Bouhaddani","doi":"10.1080/02664763.2024.2313458","DOIUrl":"https://doi.org/10.1080/02664763.2024.2313458","url":null,"abstract":"In many studies of human diseases, multiple omics datasets are measured. Typically, these omics datasets are studied one by one with the disease, thus the relationship between omics is overlooked. ...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"79 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Anticipative Bayesian classification for data streams with verification latency 针对具有验证延迟的数据流的预期贝叶斯分类法
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-21 DOI: 10.1080/02664763.2024.2319222
Vera Hofer, Georg Krempl, Dominik Lang
{"title":"Anticipative Bayesian classification for data streams with verification latency","authors":"Vera Hofer, Georg Krempl, Dominik Lang","doi":"10.1080/02664763.2024.2319222","DOIUrl":"https://doi.org/10.1080/02664763.2024.2319222","url":null,"abstract":"Most of the existing adaptive classification algorithms in non-stationary data streams require recent labelled data for their updates. Such recent labels are often missing. For stream classificatio...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"44 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139955543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Multiple observers ranked set samples for shrinkage estimators 收缩估计器的多观察者排序集合样本
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-16 DOI: 10.1080/02664763.2024.2317312
Andrew David Pearce, Armin Hatefi
{"title":"Multiple observers ranked set samples for shrinkage estimators","authors":"Andrew David Pearce, Armin Hatefi","doi":"10.1080/02664763.2024.2317312","DOIUrl":"https://doi.org/10.1080/02664763.2024.2317312","url":null,"abstract":"Ranked set sampling (RSS) is used as a powerful data collection technique for situations where measuring the study variable requires a costly and/or tedious process while the sampling units can be ...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139752033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Developing predictive precision medicine models by exploiting real-world data using machine learning methods 使用机器学习方法，利用真实世界的数据开发预测性精准医疗模型
IF 1.5 4区 数学
Journal of Applied Statistics Pub Date : 2024-02-13 DOI: 10.1080/02664763.2024.2315451
Panagiotis C. Theocharopoulos, Sotiris Bersimis, Spiros V. Georgakopoulos, Antonis Karaminas, Sotiris K. Tasoulis, Vassilis P. Plagianakos
{"title":"Developing predictive precision medicine models by exploiting real-world data using machine learning methods","authors":"Panagiotis C. Theocharopoulos, Sotiris Bersimis, Spiros V. Georgakopoulos, Antonis Karaminas, Sotiris K. Tasoulis, Vassilis P. Plagianakos","doi":"10.1080/02664763.2024.2315451","DOIUrl":"https://doi.org/10.1080/02664763.2024.2315451","url":null,"abstract":"Computational Medicine encompasses the application of Statistical Machine Learning and Artificial Intelligence methods on several traditional medical approaches, including biochemical testing which...","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"44 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139752220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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