Statistics and Computing最新文献

Efficient Inference in First Passage Time Models. 第一通道时间模型的有效推理。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2026-06-01 Epub Date: 2026-03-03 DOI: 10.1007/s11222-026-10854-4

Sicheng Liu, Alexander Fengler, Michael J Frank, Matthew T Harrison

{"title":"Efficient Inference in First Passage Time Models.","authors":"Sicheng Liu, Alexander Fengler, Michael J Frank, Matthew T Harrison","doi":"10.1007/s11222-026-10854-4","DOIUrl":"10.1007/s11222-026-10854-4","url":null,"abstract":"First passage time models describe the time it takes for a random process to exit a region of interest and are widely used across various scientific fields. Fast and accurate numerical methods for computing the likelihood function in these models are essential for efficient statistical inference of model parameters. Specifically, in computational cognitive neuroscience, generalized drift diffusion models (GDDMs) are an important class of first passage time models that describe the latent psychological processes underlying simple decision-making scenarios. GDDMs model the joint distribution over choices and response times as the first hitting time of a one-dimensional stochastic differential equation (SDE) to possibly time-varying upper and lower boundaries. They are widely applied to extract parameters associated with distinct cognitive and neural mechanisms. However, current likelihood computation methods struggle in common application scenarios in which drift rates dynamically vary within trials as a function of exogenous covariates (e.g., brain activity in specific regions or visual fixations). In this work, we propose a fast and flexible algorithm for computing the likelihood function of GDDMs based on a large class of SDEs satisfying the Cherkasov condition. Our method divides each trial into discrete stages, employs fast analytical results to compute stage-wise densities, and integrates these to compute the overall trial-wise likelihood. Numerical examples demonstrate that our method not only yields accurate likelihood evaluations for efficient statistical inference, but also considerably outperforms existing approaches in terms of speed.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13021154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575496","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

Approximating evidence via bounded harmonic means. 通过有界谐波均值逼近证据。

IF 1.6 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-04-17 DOI: 10.1007/s11222-026-10875-z

Dana Naderi, Christian P Robert, Kaniav Kamary, Darren Wraith

{"title":"Approximating evidence via bounded harmonic means.","authors":"Dana Naderi, Christian P Robert, Kaniav Kamary, Darren Wraith","doi":"10.1007/s11222-026-10875-z","DOIUrl":"10.1007/s11222-026-10875-z","url":null,"abstract":"Efficient Bayesian model selection relies on the model evidence or marginal likelihood, whose computation often requires evaluating an intractable integral. The harmonic mean estimator (HME) has long been a standard method of approximating the evidence. While computationally simple, the version introduced by Newton and Raftery (1994) potentially suffers from infinite variance. To overcome this issue, Gelfand and Dey (1994) defined a standardized representation of the estimator based on an instrumental function and Robert and Wraith (2009) later proposed to use higher posterior density (HPD) indicators as instrumental functions. Following this approach, a practical method is proposed, based on an elliptical covering of the HPD region with non-overlapping ellipsoids. The resulting estimator, called the Elliptical Covering Marginal Likelihood Estimator (ECMLE), not only eliminates the infinite-variance issue of the original HME and allows exact volume computations, but is also able to be used in multimodal settings. Through several examples, we illustrate that ECMLE outperforms other recent methods such as THAMES and its improved version (Metodiev et al. 2025). Moreover, ECMLE demonstrates lower variance-a key challenge that subsequent HME variants have sought to address-and provides more stable evidence approximations, even in challenging settings.Supplementary information: The online version contains supplementary material available at 10.1007/s11222-026-10875-z.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 3","pages":"120"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13090188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724021","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

Unifying Summary Statistic Selection for Approximate Bayesian Computation. 近似贝叶斯计算的统一汇总统计选择。

IF 1.6 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-01-27 DOI: 10.1007/s11222-025-10808-2

Till Hoffmann, Jukka-Pekka Onnela

{"title":"Unifying Summary Statistic Selection for Approximate Bayesian Computation.","authors":"Till Hoffmann, Jukka-Pekka Onnela","doi":"10.1007/s11222-025-10808-2","DOIUrl":"10.1007/s11222-025-10808-2","url":null,"abstract":"Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and demonstrate their importance for correctly analyzing dimensionality reduction algorithms. We demonstrate that minimizing the expected posterior entropy (EPE) under the prior predictive distribution of the model provides a unifying principle that subsumes many existing methods; they are shown to be equivalent to, or special or limiting cases of, minimizing the EPE. We offer a unifying framework for obtaining informative summaries and propose a practical method using conditional density estimation to learn high-fidelity summaries automatically. We evaluate this approach on diverse problems, including a challenging benchmark model with a multi-modal posterior, a population genetics model, and a dynamic network model of growing trees. The results show that EPE-minimizing summaries can lead to posterior inference that is competitive with, and in some cases superior to, dedicated likelihood-based approaches, providing a powerful and general tool for practitioners.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 2","pages":"70"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12847231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146087359","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

Outlier detection in state-space models using mean-shift penalisation. 使用均值偏移惩罚的状态空间模型中的离群值检测。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-07-13 DOI: 10.1007/s11222-026-10935-4

Rajan Shankar, Ines Wilms, Jakob Raymaekers, Garth Tarr

{"title":"Outlier detection in state-space models using mean-shift penalisation.","authors":"Rajan Shankar, Ines Wilms, Jakob Raymaekers, Garth Tarr","doi":"10.1007/s11222-026-10935-4","DOIUrl":"10.1007/s11222-026-10935-4","url":null,"abstract":"State-space models (SSMs) provide a flexible framework for modelling time series data, but their reliance on Gaussian error assumptions makes them highly sensitive to outliers. We propose a robust estimation method, ROAMS, that mitigates the influence of additive outliers by introducing shift parameters at each timepoint in the observation equation of the SSM. These parameters allow the model to attribute non-zero shifts to outliers while leaving clean observations unaffected. ROAMS then enables automatic outlier detection, through the addition of a penalty term on the number of flagged outlying timepoints in the loss function, and simultaneous estimation of model parameters. We apply the method to robustly estimate SSMs on both simulated data and real-world animal location-tracking data, demonstrating its ability to produce more reliable parameter estimates than classical methods and other benchmark methods. In addition to improved robustness, ROAMS offers practical diagnostic tools, including BIC curves for selecting tuning parameters and visualising outlier structure. These features make our approach broadly useful for researchers and practitioners working with contaminated time series data.Supplementary information: The online version contains supplementary material available at 10.1007/s11222-026-10935-4.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 4","pages":"176"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449625","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

A Support vector machine-based mixture cure model for mixed case interval censored data. 基于支持向量机的混合病例间隔截尾数据混合修复模型。

IF 1.6 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-01-16 DOI: 10.1007/s11222-025-10796-3

Suvra Pal, Wisdom Aselisewine

{"title":"A Support vector machine-based mixture cure model for mixed case interval censored data.","authors":"Suvra Pal, Wisdom Aselisewine","doi":"10.1007/s11222-025-10796-3","DOIUrl":"10.1007/s11222-025-10796-3","url":null,"abstract":"We propose a semi-parametric two-component model for the analysis of mixed case interval censored (MCIC) data with a cured subgroup. Such data occurs when the time to an event of interest is only known to belong to an interval obtained from a sequence of, say, k random examination time points with k representing an integer. Furthermore, there is a proportion of subjects who would never be susceptible to the event. The first component of the proposed model describes the probability of cure, and it replaces the traditional generalized linear model with a more flexible support vector machine (SVM)-based approach capable of capturing complex covariate effects. The second component of the proposed model describes the survival distribution of the uncured and is modeled using a Cox proportional hazards structure to preserve the easy interpretation of covariate effects. To the best of our knowledge, this is the first work that employs a machine learning algorithm to analyze MCIC data in the presence of a cured subgroup. To estimate the model parameters, we develop an expectation maximization algorithm. A detailed simulation study demonstrates the superiority of the proposed SVM-based model. Finally, we analyze NASA's Hypobaric Decompression Sickness Data using the proposed approach.Supplementary information: The online version contains supplementary material available at 10.1007/s11222-025-10796-3.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 2","pages":"63"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12811344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145998515","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

Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges. 指数随机图模型的神经后验估计：评估偏差和实现挑战。

IF 1.8 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2026-05-17 DOI: 10.1007/s11222-026-10896-8

Yefeng Fan, Simon Richard White

{"title":"Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.","authors":"Yefeng Fan, Simon Richard White","doi":"10.1007/s11222-026-10896-8","DOIUrl":"10.1007/s11222-026-10896-8","url":null,"abstract":"Exponential random graph models (ERGMs) are flexible probabilistic frameworks to model statistical networks through a variety of network summary statistics. Conventional Bayesian estimation for ERGMs involves iteratively exchanging with an auxiliary variable due to the intractability of the ERGM likelihood. However, this approach has limited scalability in large-scale implementations. Neural posterior estimation (NPE) is a recent advancement in simulation-based inference, using a neural network-based density estimator to infer the posterior for models with doubly intractable likelihoods for which simulations can be generated. While NPE has been successfully adopted in various fields such as cosmology, little research has investigated its use for ERGMs. Performing NPE on ERGMs not only provides a different approach to estimation for intractable ERGM likelihoods but also allows more efficient and scalable inference using the amortisation properties of NPE, and therefore we investigate how NPE can be effectively implemented in ERGMs. In this study, we present the first systematic implementation of NPE for ERGMs, rigorously evaluating potential biases, interpreting the bias magnitudes, and assessing computational costs. We compare NPE fits with conventional Bayesian ERGM fits as well as related neural simulation-based methods, namely neural likelihood estimation and neural ratio estimation. In our synthetic data analysis, we show that training a neural posterior estimator on 500,000 simulations circumvents the roughly 4,000,000,000 simulations required by conventional exchange-algorithm inference, enabling real-time posterior estimation. More importantly, our work highlights ERGM-specific areas that may pose particular challenges for the adoption of NPE.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"36 4","pages":"141"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13180768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147975723","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

Model-based clustering of time-dependent observations with common structural changes. 具有共同结构变化的时变观测的基于模型的聚类。

IF 1.6 2区数学

Statistics and Computing Pub Date : 2026-01-01 Epub Date: 2025-10-28 DOI: 10.1007/s11222-025-10756-x

Riccardo Corradin, Luca Danese, Wasiur R KhudaBukhsh, Andrea Ongaro

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