Statistics and Computing最新文献_第2页

IF 1.8 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-09-27 DOI: 10.1007/s11222-026-10979-6

Ivis Kerama, Thomas Thorne, Richard G Everitt

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Rare event ABC-SMC <ns0:math><ns0:mmultiscripts><ns0:mrow /> <ns0:mrow /> <ns0:mn>2</ns0:mn></ns0:mmultiscripts></ns0:math>.","authors":"Ivis Kerama, Thomas Thorne, Richard G Everitt","doi":"10.1007/s11222-026-10979-6","DOIUrl":"10.1007/s11222-026-10979-6","url":null,"abstract":"Approximate Bayesian computation (ABC) is a well-established family of Monte Carlo methods for performing approximate Bayesian inference in the case where an \"implicit\" model is used for the data: when the data model can be simulated, but the likelihood cannot easily be pointwise evaluated. A fundamental property of standard ABC approaches is that the number of Monte Carlo points required to achieve a given accuracy scales exponentially with the dimension of the data. Prangle et al. (2018) proposes a Markov chain Monte Carlo (MCMC) method that uses a rare event sequential Monte Carlo (SMC) approach to estimating the ABC likelihood that avoids this exponential scaling, and thus allows ABC to be used on higher dimensional data. This paper builds on the work of Prangle et al. (2018) by using the rare event SMC approach within an SMC algorithm, instead of within an MCMC algorithm. The new method has a similar structure to SMC <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>2</mn></mmultiscripts> </math> (Chopin et al. 2013), and requires less tuning than the MCMC approach. We demonstrate the new approach, compared to existing ABC-SMC methods, on a toy example and on a duplication-divergence random graph model used for modelling protein interaction networks.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 5","pages":"225"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13616861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi. 离散值状态转移模型的非定心：马拉维产esbl大肠杆菌传播的应用。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-08-20 DOI: 10.1007/s11222-026-10956-z

James Neill, Rebecca Lester, Winnie Bakali, Gareth Roberts, Nicholas Feasey, Lloyd A C Chapman, Chris Jewell

{"title":"Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi.","authors":"James Neill, Rebecca Lester, Winnie Bakali, Gareth Roberts, Nicholas Feasey, Lloyd A C Chapman, Chris Jewell","doi":"10.1007/s11222-026-10956-z","DOIUrl":"10.1007/s11222-026-10956-z","url":null,"abstract":"Infectious disease transmission is often modelled by discrete-valued stochastic state transition processes. Due to a lack of complete data, Bayesian inference for these models often relies on data-augmentation techniques. These techniques are often inefficient or time consuming to implement. We introduce a novel data-augmentation Markov chain Monte Carlo method for discrete-time individual-based epidemic models, which we call the Rippler algorithm. This method uses the transmission model in the proposal step of the Metropolis-Hastings algorithm, rather than in the accept-reject step. We test the Rippler algorithm on simulated data and apply it to data on extended-spectrum beta-lactamase (ESBL)-producing E. coli collected in Blantyre, Malawi. We compare the Rippler algorithm to two other commonly used Bayesian inference methods for partially observed epidemic data, and find that it has a good balance between mixing speed and computational complexity.Supplementary information: The online version contains supplementary material available at 10.1007/s11222-026-10956-z.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 5","pages":"208"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13493405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148798062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-negative matrix factorization algorithms generally improve topic model fits. 非负矩阵分解算法通常可以提高主题模型的拟合性。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-05-05 DOI: 10.1007/s11222-026-10866-0

Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens

引用次数: 0

Accelerated inference for stochastic compartmental models with over-dispersed partial observations. 过度分散部分观测的随机区室模型的加速推理。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-03-22 DOI: 10.1007/s11222-026-10865-1

Michael Whitehouse

{"title":"Accelerated inference for stochastic compartmental models with over-dispersed partial observations.","authors":"Michael Whitehouse","doi":"10.1007/s11222-026-10865-1","DOIUrl":"10.1007/s11222-026-10865-1","url":null,"abstract":"An assumed density approximate likelihood is derived for a class of partially observed stochastic compartmental models which permit observational over-dispersion. This is achieved by treating time-varying reporting probabilities as latent variables and integrating them out using Laplace approximations within Poisson Approximate Likelihoods (LawPAL), resulting in a fast deterministic approximation to the marginal likelihood and filtering distributions. We derive an asymptotically exact filtering result in the large population regime, demonstrating the approximation's ability to recover latent disease states and reporting probabilities. Through simulations we: 1) demonstrate favorable behavior of the maximum approximate likelihood estimator in the large population and time horizon regime in terms of ground truth recovery; 2) demonstrate order of magnitude computational speed gains over a sequential Monte Carlo likelihood based approach and explore the statistical compromises our approximation implicitly makes. We conclude by embedding our methodology within the probabilistic programming language Stan for automated Bayesian inference to develop a model of practical interest using data from the Covid-19 outbreak in Switzerland.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 3","pages":"109"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13006463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147514939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subgroup Analysis of Differential Networks with Latent Variables. 具有潜在变量的微分网络的亚群分析。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2025-10-01 Epub Date: 2025-07-02 DOI: 10.1007/s11222-025-10681-z

Linxi Li, Shuangge Ma, Qingzhao Zhang

{"title":"Subgroup Analysis of Differential Networks with Latent Variables.","authors":"Linxi Li, Shuangge Ma, Qingzhao Zhang","doi":"10.1007/s11222-025-10681-z","DOIUrl":"10.1007/s11222-025-10681-z","url":null,"abstract":"Differential network analysis serves as a crucial tool in capturing variations in network rewiring patterns across different biological conditions. Real-world observational data often contain subgroup structures with differing statistical properties, potentially leading to heterogeneity among differential networks. However, existing graphical model-based heterogeneity analysis methods are designed for subgroup networks rather than differential networks. Moreover, these methods require sparsity within each network, which is ineffective for dense networks, especially when observed variables are confounded by latent (unobserved) variables. In this article, we focus on estimating differential networks between an unlabeled heterogeneous group and a baseline group, and develop subgroup analysis from the perspective of differential networks. By imposing a sparse plus low-rank structure on the baseline network and sparsity on differential networks, which characterize and balance the influence of latent variables, the proposed method allows for effective estimation of sparse differential networks and non-sparse subgroup networks. We develop an efficient computational algorithm for this purpose. Simulation studies demonstrate the competitive performance of the proposed approach over closely related alternatives. An application to a set of data on Non-Small Cell Lung Cancer (NSCLC) further confirms the utility of the methodology.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"35 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13166169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147933391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal F-score Matching for Bipartite Record Linkage. 二部记录链接的最优f -分数匹配。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2025-10-01 Epub Date: 2025-07-31 DOI: 10.1007/s11222-025-10701-y

Eric A Bai, Olivier Binette, Jerome P Reiter

引用次数: 0

A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model. 基于加速失效时间的神经网络混合固化模型。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2025-10-01 Epub Date: 2025-06-22 DOI: 10.1007/s11222-025-10674-y

Wisdom Aselisewine, Suvra Pal

引用次数: 0

Bootstrap estimation of the proportion of outliers in robust regression. 稳健回归中异常值比例的自举估计。

IF 1.6 2区数学

Statistics and Computing Pub Date : 2025-02-01 Epub Date: 2024-11-16 DOI: 10.1007/s11222-024-10526-1

Qiang Heng, Kenneth Lange

引用次数: 0

Simulation based composite likelihood. 基于模拟的复合可能性。

IF 1.6 2区数学

Statistics and Computing Pub Date : 2025-01-01 Epub Date: 2025-02-25 DOI: 10.1007/s11222-025-10584-z

Lorenzo Rimella, Chris Jewell, Paul Fearnhead

引用次数: 0

Sequential Bayesian Registration for Functional Data. 功能数据的顺序贝叶斯配准。

IF 1.6 2区数学

Statistics and Computing Pub Date : 2025-01-01 Epub Date: 2025-05-27 DOI: 10.1007/s11222-025-10640-8

Yoonji Kim, Oksana A Chkrebtii, Sebastian A Kurtek

{"title":"Sequential Bayesian Registration for Functional Data.","authors":"Yoonji Kim, Oksana A Chkrebtii, Sebastian A Kurtek","doi":"10.1007/s11222-025-10640-8","DOIUrl":"10.1007/s11222-025-10640-8","url":null,"abstract":"In many modern applications, discretely-observed data may be naturally understood as a set of functions. Functional data often exhibit two confounded sources of variability: amplitude (y-axis) and phase (x-axis). The extraction of amplitude and phase, a process known as registration, is essential in exploring the underlying structure of functional data in a variety of areas, from environmental monitoring to medical imaging. Critically, such data are often gathered sequentially with new functional observations arriving over time. Despite this, existing registration procedures do not sequentially update inference based on the new data, requiring model refitting. To address these challenges, we introduce a Bayesian framework for sequential registration of functional data, which updates statistical inference as new sets of functions are assimilated. This Bayesian model-based sequential learning approach utilizes sequential Monte Carlo sampling to recursively update the alignment of observed functions while accounting for associated uncertainty. Distributed computing significantly reduces computational cost relative to refitting the model using an iterative method such as Markov chain Monte Carlo on the full data. Simulation studies and comparisons reveal that the proposed approach performs well even when the target posterior distribution has a challenging structure. We apply the proposed method to three real datasets: (1) functions of annual drought intensity near Kaweah River in California, (2) annual sea surface salinity functions near Null Island, and (3) a sequence of repeated patterns in electrocardiogram signals.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"35 4","pages":"108"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0