IF 1.7 4区数学

Stat Pub Date : 2024-07-26 DOI: 10.1002/sta4.718

Julia Sharp, Emily H. Griffith, Bruce A. Craig, Alexandra Hanlon, Sarah Peskoe, Jennifer Van Mullekom

引用次数: 0

New two‐sample test utilizing interpoint distance discrepancy 利用点间距离差异进行新的双样本检验

IF 1.7 4区数学

Stat Pub Date : 2024-07-22 DOI: 10.1002/sta4.712

Dong Xu

引用次数: 0

Sign‐flip inference for spatial regression with differential regularisation 利用微分正则化进行空间回归的符号翻转推理

IF 1.7 4区数学

Stat Pub Date : 2024-07-17 DOI: 10.1002/sta4.711

Michele Cavazzutti, Eleonora Arnone, Federico Ferraccioli, Cristina Galimberti, Livio Finos, Laura M. Sangalli

{"title":"Sign‐flip inference for spatial regression with differential regularisation","authors":"Michele Cavazzutti, Eleonora Arnone, Federico Ferraccioli, Cristina Galimberti, Livio Finos, Laura M. Sangalli","doi":"10.1002/sta4.711","DOIUrl":"https://doi.org/10.1002/sta4.711","url":null,"abstract":"SummaryWe address the problem of performing inference on the linear and nonlinear terms of a semiparametric spatial regression model with differential regularisation. For the linear term, we propose a new resampling procedure, based on (partial) sign‐flipping of an appropriate transformation of the residuals of the model. The proposed resampling scheme can mitigate the bias effect induced by the differential regularisation. We prove that the proposed test is asymptotically exact. Moreover, we show, by simulation studies, that it enjoys very good control of Type‐I error also in small sample scenarios, differently from parametric alternatives. Additionally, we show that the proposed test has higher power with respect than recently proposed nonparametric tests on the linear term of semiparametric regression models with differential regularisation. Concerning the nonlinear term, we develop three different inference approaches: a parametric one and two nonparametric alternatives. The nonparametric tests are based on a sign‐flip approach. One of these is proved to be asymptotically exact, while the other is proved to be exact also for finite samples. Simulation studies highlight the good control of Type‐I error of the nonparametric approaches with respect the parametric test, while retaining high power.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739153","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 Markov‐switching spatio‐temporal ARCH model 马尔可夫转换时空 ARCH 模型

IF 1.7 4区数学

Stat Pub Date : 2024-07-15 DOI: 10.1002/sta4.713

Tzung Hsuen Khoo, Dharini Pathmanathan, Philipp Otto, Sophie Dabo‐Niang

{"title":"A Markov‐switching spatio‐temporal ARCH model","authors":"Tzung Hsuen Khoo, Dharini Pathmanathan, Philipp Otto, Sophie Dabo‐Niang","doi":"10.1002/sta4.713","DOIUrl":"https://doi.org/10.1002/sta4.713","url":null,"abstract":"Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720613","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

Using sliced inverse mean difference for dimension reduction in multivariate time series 在多元时间序列中使用切片反平均差降维

IF 1.7 4区数学

Stat Pub Date : 2024-07-13 DOI: 10.1002/sta4.709

Hector Haffenden, Andreas Artemiou

引用次数: 0

What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure 你们说你们在这里做什么？宣传数据科学家在研究基础设施中的重要作用

IF 1.7 4区数学

Stat Pub Date : 2024-07-11 DOI: 10.1002/sta4.714

Chasz Griego, Nicky Agate, Ana‐Maria Iosif, Amy M. Crisp

{"title":"What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure","authors":"Chasz Griego, Nicky Agate, Ana‐Maria Iosif, Amy M. Crisp","doi":"10.1002/sta4.714","DOIUrl":"https://doi.org/10.1002/sta4.714","url":null,"abstract":"Clinical and academic research continues to become more complex as our knowledge and technology advance. A substantial and growing number of specialists in biostatistics, data science and library sciences are needed to support these research systems and promote high‐calibre research. However, that support is often marginalized as optional rather than a fundamental component of research infrastructure. By building research infrastructure, an institution harnesses access to tools and support/service centres that host skilled experts who approach research with best practices in mind and domain‐specific knowledge at hand. We outline the potential roles of data scientists and statisticians in research infrastructure and recommend guidelines for advocating for the institutional resources needed to support these roles in a sustainable and efficient manner for the long‐term success of the institution. We provide these guidelines in terms of resource efficiency, monetary efficiency and long‐term sustainability. We hope this work contributes to—and provides shared language for—a conversation on a broader framework beyond metrics that can be used to advocate for needed resources.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613775","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 sharper bound of the Hotelling–Solomons inequality 霍特林-索洛蒙斯不等式的更尖锐约束

IF 1.7 4区数学

Stat Pub Date : 2024-07-09 DOI: 10.1002/sta4.710

Yuzo Maruyama

引用次数: 0

Tensor factor adjustment for image classification with pervasive noises 张量因子调整用于具有普遍噪声的图像分类

IF 1.7 4区数学

Stat Pub Date : 2024-06-27 DOI: 10.1002/sta4.705

Xiaochuan Li, Bingnan Li, Wenzhan Song, Yuan Ke

引用次数: 0

Solving the missing at random problem in semi‐supervised learning: An inverse probability weighting method 解决半监督学习中的随机缺失问题：反概率加权法