arXiv - STAT - Methodology最新文献_第6页

An efficient heuristic for approximate maximum flow computations 近似最大流量计算的高效启发式方法

arXiv - STAT - Methodology Pub Date : 2024-09-12 DOI: arxiv-2409.08350

Jingyun Qian, Georg Hahn

引用次数: 0

Debiased high-dimensional regression calibration for errors-in-variables log-contrast models 变量误差对数对比模型的去偏高维回归校准

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07568

Huali Zhao, Tianying Wang

引用次数: 0

Order selection in GARMA models for count time series: a Bayesian perspective 计数时间序列 GARMA 模型中的阶次选择：贝叶斯视角

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07263

Katerine Zuniga Lastra, Guilherme Pumi, Taiane Schaedler Prass

{"title":"Order selection in GARMA models for count time series: a Bayesian perspective","authors":"Katerine Zuniga Lastra, Guilherme Pumi, Taiane Schaedler Prass","doi":"arxiv-2409.07263","DOIUrl":"https://doi.org/arxiv-2409.07263","url":null,"abstract":"Estimation in GARMA models has traditionally been carried out under the\u0000frequentist approach. To date, Bayesian approaches for such estimation have\u0000been relatively limited. In the context of GARMA models for count time series,\u0000Bayesian estimation achieves satisfactory results in terms of point estimation.\u0000Model selection in this context often relies on the use of information\u0000criteria. Despite its prominence in the literature, the use of information\u0000criteria for model selection in GARMA models for count time series have been\u0000shown to present poor performance in simulations, especially in terms of their\u0000ability to correctly identify models, even under large sample sizes. In this\u0000study, we study the problem of order selection in GARMA models for count time\u0000series, adopting a Bayesian perspective through the application of the\u0000Reversible Jump Markov Chain Monte Carlo approach. Monte Carlo simulation\u0000studies are conducted to assess the finite sample performance of the developed\u0000ideas, including point and interval inference, sensitivity analysis, effects of\u0000burn-in and thinning, as well as the choice of related priors and\u0000hyperparameters. Two real-data applications are presented, one considering\u0000automobile production in Brazil and the other considering bus exportation in\u0000Brazil before and after the COVID-19 pandemic, showcasing the method's\u0000capabilities and further exploring its flexibility.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196469","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

Spatial Deep Convolutional Neural Networks 空间深度卷积神经网络

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07559

Qi Wang, Paul A. Parker, Robert B. Lund

引用次数: 0

Local Sequential MCMC for Data Assimilation with Applications in Geoscience 应用于地球科学数据同化的局部序列 MCMC

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07111

Hamza Ruzayqat, Omar Knio

{"title":"Local Sequential MCMC for Data Assimilation with Applications in Geoscience","authors":"Hamza Ruzayqat, Omar Knio","doi":"arxiv-2409.07111","DOIUrl":"https://doi.org/arxiv-2409.07111","url":null,"abstract":"This paper presents a new data assimilation (DA) scheme based on a sequential\u0000Markov Chain Monte Carlo (SMCMC) DA technique [Ruzayqat et al. 2024] which is\u0000provably convergent and has been recently used for filtering, particularly for\u0000high-dimensional non-linear, and potentially, non-Gaussian state-space models.\u0000Unlike particle filters, which can be considered exact methods and can be used\u0000for filtering non-linear, non-Gaussian models, SMCMC does not assign weights to\u0000the samples/particles, and therefore, the method does not suffer from the issue\u0000of weight-degeneracy when a relatively small number of samples is used. We\u0000design a localization approach within the SMCMC framework that focuses on\u0000regions where observations are located and restricts the transition densities\u0000included in the filtering distribution of the state to these regions. This\u0000results in immensely reducing the effective degrees of freedom and thus\u0000improving the efficiency. We test the new technique on high-dimensional ($d\u0000sim 10^4 - 10^5$) linear Gaussian model and non-linear shallow water models\u0000with Gaussian noise with real and synthetic observations. For two of the\u0000numerical examples, the observations mimic the data generated by the Surface\u0000Water and Ocean Topography (SWOT) mission led by NASA, which is a swath of\u0000ocean height observations that changes location at every assimilation time\u0000step. We also use a set of ocean drifters' real observations in which the\u0000drifters are moving according the ocean kinematics and assumed to have\u0000uncertain locations at the time of assimilation. We show that when higher\u0000accuracy is required, the proposed algorithm is superior in terms of efficiency\u0000and accuracy over competing ensemble methods and the original SMCMC filter.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196632","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

Clustered Factor Analysis for Multivariate Spatial Data 多变量空间数据的聚类因子分析

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07018

Yanxiu Jin, Tomoya Wakayama, Renhe Jiang, Shonosuke Sugasawa

引用次数: 0

Non-parametric estimation of transition intensities in interval censored Markov multi-state models without loops 无循环区间删失马尔可夫多态模型中过渡强度的非参数估计

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07176

Daniel Gomon, Hein Putter

引用次数: 0

Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments 用于安全环境的动态贝叶斯网络、诱导和数据嵌入

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07389

Kieran Drury, Jim Q. Smith

{"title":"Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments","authors":"Kieran Drury, Jim Q. Smith","doi":"arxiv-2409.07389","DOIUrl":"https://doi.org/arxiv-2409.07389","url":null,"abstract":"Serious crime modelling typically needs to be undertaken securely behind a\u0000firewall where police knowledge and capabilities can remain undisclosed. Data\u0000informing an ongoing incident is often sparse, with a large proportion of\u0000relevant data only coming to light after the incident culminates or after\u0000police intervene - by which point it is too late to make use of the data to aid\u0000real-time decision making for the incident in question. Much of the data that\u0000is available to police to support real-time decision making is highly\u0000confidential so cannot be shared with academics, and is therefore missing to\u0000them. In this paper, we describe the development of a formal protocol where a\u0000graphical model is used as a framework for securely translating a model\u0000designed by an academic team to a model for use by a police team. We then show,\u0000for the first time, how libraries of these models can be built and used for\u0000real-time decision support to circumvent the challenges of data missingness and\u0000tardiness seen in such a secure environment. The parallel development described\u0000by this protocol ensures that any sensitive information collected by police,\u0000and missing to academics, remains secured behind a firewall. The protocol\u0000nevertheless guides police so that they are able to combine the typically\u0000incomplete data streams that are open source with their more sensitive\u0000information in a formal and justifiable way. We illustrate the application of\u0000this protocol by describing how a new entry - a suspected vehicle attack - can\u0000be embedded into such a police library of criminal plots.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225165","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

Local Effects of Continuous Instruments without Positivity 无正向性连续仪器的局部效应

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07350

Prabrisha Rakshit, Alexander Levis, Luke Keele

{"title":"Local Effects of Continuous Instruments without Positivity","authors":"Prabrisha Rakshit, Alexander Levis, Luke Keele","doi":"arxiv-2409.07350","DOIUrl":"https://doi.org/arxiv-2409.07350","url":null,"abstract":"Instrumental variables have become a popular study design for the estimation\u0000of treatment effects in the presence of unobserved confounders. In the\u0000canonical instrumental variables design, the instrument is a binary variable,\u0000and most extant methods are tailored to this context. In many settings,\u0000however, the instrument is a continuous measure. Standard estimation methods\u0000can be applied with continuous instruments, but they require strong assumptions\u0000regarding functional form. Moreover, while some recent work has introduced more\u0000flexible approaches for continuous instruments, these methods require an\u0000assumption known as positivity that is unlikely to hold in many applications.\u0000We derive a novel family of causal estimands using a stochastic dynamic\u0000intervention framework that considers a range of intervention distributions\u0000that are absolutely continuous with respect to the observed distribution of the\u0000instrument. These estimands focus on a specific form of local effect but do not\u0000require a positivity assumption. Next, we develop doubly robust estimators for\u0000these estimands that allow for estimation of the nuisance functions via\u0000nonparametric estimators. We use empirical process theory and sample splitting\u0000to derive asymptotic properties of the proposed estimators under weak\u0000conditions. In addition, we derive methods for profiling the principal strata\u0000as well as a method for sensitivity analysis for assessing robustness to an\u0000underlying monotonicity assumption. We evaluate our methods via simulation and\u0000demonstrate their feasibility using an application on the effectiveness of\u0000surgery for specific emergency conditions.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225123","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

Determining number of factors under stability considerations 在考虑稳定性的情况下确定因子数量

arXiv - STAT - Methodology Pub Date : 2024-09-11 DOI: arxiv-2409.07617

Sze Ming Lee, Yunxiao Chen

引用次数: 0