arXiv - STAT - Methodology最新文献_第4页

Exact Posterior Mean and Covariance for Generalized Linear Mixed Models 广义线性混合模型的精确后验均值和协方差

arXiv - STAT - Methodology Pub Date : 2024-09-14 DOI: arxiv-2409.09310

Tonglin Zhang

引用次数: 0

Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices 结合半参数 SAR 模型和 GAMLSS 模型为对冲价格建立均值和非均质方差的联合空间模型

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08912

J. D. Toloza-Delgado, O. O. Melo, N. A. Cruz

{"title":"Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices","authors":"J. D. Toloza-Delgado, O. O. Melo, N. A. Cruz","doi":"arxiv-2409.08912","DOIUrl":"https://doi.org/arxiv-2409.08912","url":null,"abstract":"In the context of spatial econometrics, it is very useful to have\u0000methodologies that allow modeling the spatial dependence of the observed\u0000variables and obtaining more precise predictions of both the mean and the\u0000variability of the response variable, something very useful in territorial\u0000planning and public policies. This paper proposes a new methodology that\u0000jointly models the mean and the variance. Also, it allows to model the spatial\u0000dependence of the dependent variable as a function of covariates and to model\u0000the semiparametric effects in both models. The algorithms developed are based\u0000on generalized additive models that allow the inclusion of non-parametric terms\u0000in both the mean and the variance, maintaining the traditional theoretical\u0000framework of spatial regression. The theoretical developments of the estimation\u0000of this model are carried out, obtaining desirable statistical properties in\u0000the estimators. A simulation study is developed to verify that the proposed\u0000method has a remarkable predictive capacity in terms of the mean square error\u0000and shows a notable improvement in the estimation of the spatial autoregressive\u0000parameter, compared to other traditional methods and some recent developments.\u0000The model is also tested on data from the construction of a hedonic price model\u0000for the city of Bogota, highlighting as the main result the ability to model\u0000the variability of housing prices, and the wealth in the analysis obtained.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi forests: Variable importance for multi-class outcomes 多重森林：多类结果的变量重要性

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08925

Roman HornungInstitute for Medical Information Processing, Biometry and Epidemiology, LMU Munich, Munich, GermanyMunich Center for Machine Learning, Alexander HapfelmeierInstitute of AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany

{"title":"Multi forests: Variable importance for multi-class outcomes","authors":"Roman HornungInstitute for Medical Information Processing, Biometry and Epidemiology, LMU Munich, Munich, GermanyMunich Center for Machine Learning, Alexander HapfelmeierInstitute of AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany","doi":"arxiv-2409.08925","DOIUrl":"https://doi.org/arxiv-2409.08925","url":null,"abstract":"In prediction tasks with multi-class outcomes, identifying covariates\u0000specifically associated with one or more outcome classes can be important.\u0000Conventional variable importance measures (VIMs) from random forests (RFs),\u0000like permutation and Gini importance, focus on overall predictive performance\u0000or node purity, without differentiating between the classes. Therefore, they\u0000can be expected to fail to distinguish class-associated covariates from\u0000covariates that only distinguish between groups of classes. We introduce a VIM\u0000called multi-class VIM, tailored for identifying exclusively class-associated\u0000covariates, via a novel RF variant called multi forests (MuFs). The trees in\u0000MuFs use both multi-way and binary splitting. The multi-way splits generate\u0000child nodes for each class, using a split criterion that evaluates how well\u0000these nodes represent their respective classes. This setup forms the basis of\u0000the multi-class VIM, which measures the discriminatory ability of the splits\u0000performed in the respective covariates with regard to this split criterion.\u0000Alongside the multi-class VIM, we introduce a second VIM, the discriminatory\u0000VIM. This measure, based on the binary splits, assesses the strength of the\u0000general influence of the covariates, irrespective of their\u0000class-associatedness. Simulation studies demonstrate that the multi-class VIM\u0000specifically ranks class-associated covariates highly, unlike conventional VIMs\u0000which also rank other types of covariates highly. Analyses of 121 datasets\u0000reveal that MuFs often have slightly lower predictive performance compared to\u0000conventional RFs. This is, however, not a limiting factor given the algorithm's\u0000primary purpose of calculating the multi-class VIM.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tracing the impacts of Mount Pinatubo eruption on global climate using spatially-varying changepoint detection 利用空间变化点探测追踪皮纳图博火山爆发对全球气候的影响

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08908

Samantha Shi-Jun, Lyndsay Shand, Bo Li

引用次数: 0

Cubature-based uncertainty estimation for nonlinear regression models 基于三次方的非线性回归模型不确定性估计

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08756

Martin Bubel, Jochen Schmid, Maximilian Carmesin, Volodymyr Kozachynskyi, Erik Esche, Michael Bortz

引用次数: 0

Angular Co-variance using intrinsic geometry of torus: Non-parametric change points detection in meteorological data 利用环的固有几何形状进行角度共变：气象数据中的非参数变化点检测

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08838

Surojit Biswas, Buddhananda Banerjee, Arnab Kumar Laha

{"title":"Angular Co-variance using intrinsic geometry of torus: Non-parametric change points detection in meteorological data","authors":"Surojit Biswas, Buddhananda Banerjee, Arnab Kumar Laha","doi":"arxiv-2409.08838","DOIUrl":"https://doi.org/arxiv-2409.08838","url":null,"abstract":"In many temporal datasets, the parameters of the underlying distribution may\u0000change abruptly at unknown times. Detecting these changepoints is crucial for\u0000numerous applications. While this problem has been extensively studied for\u0000linear data, there has been remarkably less research on bivariate angular data.\u0000For the first time, we address the changepoint problem for the mean direction\u0000of toroidal and spherical data, which are types of bivariate angular data. By\u0000leveraging the intrinsic geometry of a curved torus, we introduce the concept\u0000of the ``square'' of an angle. This leads us to define the ``curved dispersion\u0000matrix'' for bivariate angular random variables, analogous to the dispersion\u0000matrix for bivariate linear random variables. Using this analogous measure of\u0000the ``Mahalanobis distance,'' we develop two new non-parametric tests to\u0000identify changes in the mean direction parameters for toroidal and spherical\u0000distributions. We derive the limiting distributions of the test statistics and\u0000evaluate their power surface and contours through extensive simulations. We\u0000also apply the proposed methods to detect changes in mean direction for hourly\u0000wind-wave direction measurements and the path of the cyclonic storm\u0000``Biporjoy,'' which occurred between 6th and 19th June 2023 over the Arabian\u0000Sea, western coast of India.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of distributions for risks based on the first moment and c-statistic 基于第一矩和 c 统计量的风险分布识别

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.09178

Mohsen Sadatsafavi, Tae Yoon Lee, John Petkau

引用次数: 0

Change point analysis with irregular signals 不规则信号的变化点分析

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08863

Tobias Kley, Yuhan Philip Liu, Hongyuan Cao, Wei Biao Wu

引用次数: 0

Regression-based proximal causal inference for right-censored time-to-event data 针对右删失时间到事件数据的基于回归的近因推断

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08924

Kendrick Li, George C. Linderman, Xu Shi, Eric J. Tchetgen Tchetgen

{"title":"Regression-based proximal causal inference for right-censored time-to-event data","authors":"Kendrick Li, George C. Linderman, Xu Shi, Eric J. Tchetgen Tchetgen","doi":"arxiv-2409.08924","DOIUrl":"https://doi.org/arxiv-2409.08924","url":null,"abstract":"Unmeasured confounding is one of the major concerns in causal inference from\u0000observational data. Proximal causal inference (PCI) is an emerging\u0000methodological framework to detect and potentially account for confounding bias\u0000by carefully leveraging a pair of negative control exposure (NCE) and outcome\u0000(NCO) variables, also known as treatment and outcome confounding proxies.\u0000Although regression-based PCI is well developed for binary and continuous\u0000outcomes, analogous PCI regression methods for right-censored time-to-event\u0000outcomes are currently lacking. In this paper, we propose a novel two-stage\u0000regression PCI approach for right-censored survival data under an additive\u0000hazard structural model. We provide theoretical justification for the proposed\u0000approach tailored to different types of NCOs, including continuous, count, and\u0000right-censored time-to-event variables. We illustrate the approach with an\u0000evaluation of the effectiveness of right heart catheterization among critically\u0000ill patients using data from the SUPPORT study. Our method is implemented in\u0000the open-access R package 'pci2s'.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The underreported death toll of wars: a probabilistic reassessment from a structured expert elicitation 少报的战争死亡人数：通过结构化专家征询进行的概率重新评估

arXiv - STAT - Methodology Pub Date : 2024-09-13 DOI: arxiv-2409.08779

Paola Vesco, David Randahl, Håvard Hegre, Stina Högbladh, Mert Can Yilmaz

引用次数: 0