{"results":[{"id":"arxiv_2601.14796","title":"A Practical Guide to Modern Imputation","authors":[{"name":"Jeffrey Näf"}],"abstract":"This guide based on recent papers should help researchers avoid some of the most common pitfalls of missing value imputation imputation.","source":"arXiv","year":2026,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/2601.14796","pdf_url":"https://arxiv.org/pdf/2601.14796","is_open_access":true,"published_at":"2026-01-21T09:21:12Z","score":70},{"id":"crossref_10.1016/j.ekir.2024.11.347","title":"WCN25-4132 IL-31 and JAK-STAT Signaling in CKD-aP: Exploring Therapeutic Implication","authors":[{"name":"Mohinder Kaur"},{"name":"Prabhjot kaur Johal"},{"name":"Arun Prabhahar"},{"name":"B. Narayanan"},{"name":"Anuradha Bishnoi"},{"name":"Sanjeev Handa"},{"name":"Raja Ramachandran"},{"name":"Vinod Kumar"}],"abstract":"","source":"CrossRef","year":2025,"language":"en","subjects":null,"doi":"10.1016/j.ekir.2024.11.347","url":"https://doi.org/10.1016/j.ekir.2024.11.347","is_open_access":true,"citations":1,"published_at":"","score":69.03},{"id":"arxiv_2503.06401","title":"fastfrechet: An R package for fast implementation of Fréchet regression with distributional responses","authors":[{"name":"Alexander Coulter"},{"name":"Rebecca Lee"},{"name":"Irina Gaynanova"}],"abstract":"Distribution-as-response regression problems are gaining wider attention, especially within biomedical settings where observation-rich patient specific data sets are available, such as feature densities in CT scans (Petersen et al., 2021) actigraphy (Ghosal et al., 2023), and continuous glucose monitoring (Coulter et al., 2024; Matabuena et al., 2021). To accommodate the complex structure of such problems, Petersen and Müller (2019) proposed a regression framework called Fréchet regression which allows non-Euclidean responses, including distributional responses. This regression framework was further extended for variable selection by Tucker et al. (2023), and Coulter et al. (2024) (arXiv:2403.00922 [stat.AP]) developed a fast variable selection algorithm for the specific setting of univariate distributional responses equipped with the 2-Wasserstein metric (2-Wasserstein space). We present \"fastfrechet\", an R package providing fast implementation of these Fréchet regression and variable selection methods in 2-Wasserstein space, with resampling tools for automatic variable selection. \"fastfrechet\" makes distribution-based Fréchet regression with resampling-supplemented variable selection readily available and highly scalable to large data sets, such as the UK Biobank (Doherty et al., 2017).","source":"arXiv","year":2025,"language":"en","subjects":["stat.CO","stat.ME"],"doi":"10.21105/joss.07925","url":"https://arxiv.org/abs/2503.06401","pdf_url":"https://arxiv.org/pdf/2503.06401","is_open_access":true,"published_at":"2025-03-09T02:42:34Z","score":69},{"id":"arxiv_2506.07582","title":"Scalable Spatiotemporal Modeling for Bicycle Count Prediction","authors":[{"name":"Rishikesh Yadav"},{"name":"Alexandra M. Schmidt"},{"name":"Aurelie Labbe"},{"name":"Pratheepa Jeganathan"},{"name":"Luis F. Miranda-Moreno"}],"abstract":"We propose a novel sparse spatiotemporal dynamic generalized linear model for efficient inference and prediction of bicycle count data. Assuming Poisson distributed counts with spacetime-varying rates, we model the log-rate using spatiotemporal intercepts, dynamic temporal covariates, and site-specific effects additively. Spatiotemporal dependence is modeled using a spacetime-varying intercept that evolves smoothly over time with spatially correlated errors, and coefficients of some temporal covariates including seasonal harmonics also evolve dynamically over time. Inference is performed following the Bayesian paradigm, and uncertainty quantification is naturally accounted for when predicting bicycle counts for unobserved locations and future times of interest. To address the challenges of high-dimensional inference of spatiotemporal data in a Bayesian setting, we develop a customized hybrid Markov Chain Monte Carlo (MCMC) algorithm. To address the computational burden of dense covariance matrices, we extend our framework to high-dimensional spatial settings using the sparse SPDE approach of Lindgren et al. (2011), demonstrating its accuracy and scalability on both synthetic data and Montreal Island bicycle datasets. The proposed approach naturally provides missing value imputations, kriging, future forecasting, spatiotemporal predictions, and inference of model components. Moreover, it provides ways to predict average annual daily bicycles (AADB), a key metric often sought when designing bicycle networks.","source":"arXiv","year":2025,"language":"en","subjects":["stat.ME","stat.AP"],"url":"https://arxiv.org/abs/2506.07582","pdf_url":"https://arxiv.org/pdf/2506.07582","is_open_access":true,"published_at":"2025-06-09T09:26:06Z","score":69},{"id":"doaj_10.46298/jtcam.9768","title":"Mesh Density and Geodesic Tortuosity in Planar Triangular Tesselations Devoted to Fracture Mechanics","authors":[{"name":"Joffrey Lhonneur"},{"name":"Nawfal Blal"},{"name":"Yann Monerie"}],"abstract":"In fracture mechanics, the mesh sensitivity is a key issue. It is particularly true concerning cohesive volumetric finite element methods in which the crack path and the overall behavior are respectively influenced by the mesh topology and the mesh density. Poisson-Delaunay tessellations parameters, including the edge length distributions, were widely studied in the literature but very few works concern the mesh density and topology in Delaunay type meshes suitable for finite element simulations, which is of crucial interest for practical use. Starting from previous results concerning Poisson-Delaunay tessellations and studying in detail the Lloyd relaxation algorithm, we propose estimates for the probability density functions of the edge length and triangle top angles sets. These estimates depend both on the intensity of the underlying point process and on an efficiency index associated to the global quality of the mesh. The global and local accuracies of these estimates are checked for various standard mesh generators. Finally the mesh density and geodesic tortuosity are estimated for standard random or structured triangular meshes typically used in finite element simulations. These results provide practical formulas to estimate bias introduced by the mesh density and topology on the results of cohesive-volumetric finite element simulations.","source":"DOAJ","year":2024,"language":"","subjects":["Mechanics of engineering. Applied mechanics"],"doi":"10.46298/jtcam.9768","url":"https://jtcam.episciences.org/9768/pdf","pdf_url":"https://jtcam.episciences.org/9768/pdf","is_open_access":true,"published_at":"","score":68},{"id":"arxiv_2302.02859","title":"A Fast Bootstrap Algorithm for Causal Inference with Large Data","authors":[{"name":"Matthew Kosko"},{"name":"Lin Wang"},{"name":"Michele Santacatterina"}],"abstract":"Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals of estimators. Its application however can be prohibitively demanding in settings involving large data. In addition, modern causal inference estimators based on machine learning and optimization techniques exacerbate the computational burden of the bootstrap. The bag of little bootstraps has been proposed in non-causal settings for large data but has not yet been applied to evaluate the properties of estimators of causal effects. In this paper, we introduce a new bootstrap algorithm called causal bag of little bootstraps for causal inference with large data. The new algorithm significantly improves the computational efficiency of the traditional bootstrap while providing consistent estimates and desirable confidence interval coverage. We describe its properties, provide practical considerations, and evaluate the performance of the proposed algorithm in terms of bias, coverage of the true 95% confidence intervals, and computational time in a simulation study. We apply it in the evaluation of the effect of hormone therapy on the average time to coronary heart disease using a large observational data set from the Women's Health Initiative.","source":"arXiv","year":2023,"language":"en","subjects":["stat.ME","stat.AP","stat.ML"],"url":"https://arxiv.org/abs/2302.02859","pdf_url":"https://arxiv.org/pdf/2302.02859","is_open_access":true,"published_at":"2023-02-06T15:26:36Z","score":67},{"id":"arxiv_2212.11679","title":"Some reflections on the test-negative design","authors":[{"name":"Ronald Meester"},{"name":"Jan Bonte"}],"abstract":"We discuss some philosophical, methodological and practical problems concerning the use of the test-negative design for COVID-19 vaccines. These problems limit the use of this design considerably.","source":"arXiv","year":2022,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/2212.11679","pdf_url":"https://arxiv.org/pdf/2212.11679","is_open_access":true,"published_at":"2022-12-22T13:12:15Z","score":66},{"id":"doaj_10.46298/jtcam.8849","title":"Effects of the microstructural uncertainties on the poroelastic and the diffusive properties of mortar","authors":[{"name":"Adrien Socié"},{"name":"Yann Monerie"},{"name":"Frédéric Péralès"}],"abstract":"The assessment of the durability of civil engineering structures subjected to several chemical attacks requires the development of chemo-poromechanical models. The mechanical and chemical degradations depend on several factors such as the initial composition of the porous medium. A multi-scale model is used to incorporate the multi-level microstructural properties of the mortar material. The present paper aims to study the effect of morphological and local material properties uncertainties on the poroelastic and diffusive properties of mortar estimated with the help of analytical homogenization. At first, the proposed model is validated for different cement paste and mortar by comparison to experimental results and micromechanical models. Secondly, based on a literature study, sensitivity and uncertainty analysis have been developed to assess the stochastic predictions of the multi-scale model. The main result highlights the predominant impact of the cement matrix phases (C-S-H) and interfacial transition area at the mortar scale. Furthermore, the sensitive analysis underlines that the material properties induce more variability than the volume fraction.","source":"DOAJ","year":2022,"language":"","subjects":["Mechanics of engineering. Applied mechanics"],"doi":"10.46298/jtcam.8849","url":"https://jtcam.episciences.org/8849/pdf","pdf_url":"https://jtcam.episciences.org/8849/pdf","is_open_access":true,"published_at":"","score":66},{"id":"arxiv_1901.07396","title":"Bayesian Prediction of Nitrate Concentration Using a Gaussian Log-Gaussian Spatial Model with Measurement Error in Explanatory Variables","authors":[{"name":"Vahid Tadayon"}],"abstract":"This article has been removed by arXiv administrators due to falsified authorship.","source":"arXiv","year":2019,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/1901.07396","pdf_url":"https://arxiv.org/pdf/1901.07396","is_open_access":true,"published_at":"2019-01-18T07:03:31Z","score":63},{"id":"arxiv_1811.04274","title":"More robust estimation of sample average treatment effects using Kernel Optimal Matching in an observational study of spine surgical interventions","authors":[{"name":"Nathan Kallus"},{"name":"Brenton Pennicooke"},{"name":"Michele Santacatterina"}],"abstract":"Inverse probability of treatment weighting (IPTW), which has been used to estimate sample average treatment effects (SATE) using observational data, tenuously relies on the positivity assumption and the correct specification of the treatment assignment model, both of which are problematic assumptions in many observational studies. Various methods have been proposed to overcome these challenges, including truncation, covariate-balancing propensity scores, and stable balancing weights. Motivated by an observational study in spine surgery, in which positivity is violated and the true treatment assignment model is unknown, we present the use of optimal balancing by Kernel Optimal Matching (KOM) to estimate SATE. By uniformly controlling the conditional mean squared error of a weighted estimator over a class of models, KOM simultaneously mitigates issues of possible misspecification of the treatment assignment model and is able to handle practical violations of the positivity assumption, as shown in our simulation study. Using data from a clinical registry, we apply KOM to compare two spine surgical interventions and demonstrate how the result matches the conclusions of clinical trials that IPTW estimates spuriously refute.","source":"arXiv","year":2018,"language":"en","subjects":["stat.ME"],"url":"https://arxiv.org/abs/1811.04274","pdf_url":"https://arxiv.org/pdf/1811.04274","is_open_access":true,"published_at":"2018-11-10T15:47:18Z","score":62},{"id":"arxiv_1811.12106","title":"Health Effects Estimation Attributed to Particulate Matter","authors":[{"name":"V. Tadayon"}],"abstract":"This article has been removed by arXiv administrators due to falsified authorship.","source":"arXiv","year":2018,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/1811.12106","pdf_url":"https://arxiv.org/pdf/1811.12106","is_open_access":true,"published_at":"2018-11-29T12:49:31Z","score":62},{"id":"arxiv_1811.12895","title":"Spatial modeling of particulate matters and emergency room visits","authors":[{"name":"V. Tadayon"}],"abstract":"This article has been removed by arXiv administrators due to falsified authorship.","source":"arXiv","year":2018,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/1811.12895","pdf_url":"https://arxiv.org/pdf/1811.12895","is_open_access":true,"published_at":"2018-11-30T17:04:52Z","score":62},{"id":"arxiv_1605.05910","title":"A Frequency Domain Test for Propriety of Complex-Valued Vector Time Series","authors":[{"name":"Swati Chandna"},{"name":"Andrew T. Walden"}],"abstract":"This paper proposes a frequency domain approach to test the hypothesis that a complex-valued vector time series is proper, i.e., for testing whether the vector time series is uncorrelated with its complex conjugate. If the hypothesis is rejected, frequency bands causing the rejection will be identified and might usefully be related to known properties of the physical processes. The test needs the associated spectral matrix which can be estimated by multitaper methods using, say, $K$ tapers. Standard asymptotic distributions for the test statistic are of no use since they would require $K \\rightarrow \\infty,$ but, as $K$ increases so does resolution bandwidth which causes spectral blurring. In many analyses $K$ is necessarily kept small, and hence our efforts are directed at practical and accurate methodology for hypothesis testing for small $K.$ Our generalized likelihood ratio statistic combined with exact cumulant matching gives very accurate rejection percentages and outperforms other methods. We also prove that the statistic on which the test is based is comprised of canonical coherencies arising from our complex-valued vector time series.Our methodology is demonstrated on ocean current data collected at different depths in the Labrador Sea. Overall this work extends results on propriety testing for complex-valued vectors to the complex-valued vector time series setting.","source":"arXiv","year":2016,"language":"en","subjects":["stat.ME","stat.AP"],"doi":"10.1109/TSP.2016.2639459","url":"https://arxiv.org/abs/1605.05910","pdf_url":"https://arxiv.org/pdf/1605.05910","is_open_access":true,"published_at":"2016-05-19T12:12:33Z","score":60},{"id":"arxiv_1304.4200","title":"Efficiency and Structure in Multinomial Inverse Regression","authors":[{"name":"Matt Taddy"}],"abstract":"This is the rejoinder for discussion of \"Multinomial Inverse Regression for Text Analysis\", Journal of the American Statistical Association 108, 2013.","source":"arXiv","year":2013,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/1304.4200","pdf_url":"https://arxiv.org/pdf/1304.4200","is_open_access":true,"published_at":"2013-04-15T19:02:15Z","score":57},{"id":"arxiv_1010.0825","title":"Discussion of: Brownian distance covariance","authors":[{"name":"Leslie Cope"}],"abstract":"Discussion on \"Brownian distance covariance\" by Gábor J. Székely, Maria L. Rizzo [arXiv:1010.0297]","source":"arXiv","year":2010,"language":"en","subjects":["stat.AP"],"doi":"10.1214/00-AOAS312C","url":"https://arxiv.org/abs/1010.0825","pdf_url":"https://arxiv.org/pdf/1010.0825","is_open_access":true,"published_at":"2010-10-05T10:08:10Z","score":54},{"id":"arxiv_1010.0828","title":"Discussion of: Brownian distance covariance","authors":[{"name":"Andrey Feuerverger"}],"abstract":"Discussion on \"Brownian distance covariance\" by Gábor J. Székely, Maria L. Rizzo [arXiv:1010.0297]","source":"arXiv","year":2010,"language":"en","subjects":["stat.AP"],"doi":"10.1214/09-AOAS312D","url":"https://arxiv.org/abs/1010.0828","pdf_url":"https://arxiv.org/pdf/1010.0828","is_open_access":true,"published_at":"2010-10-05T10:14:35Z","score":54},{"id":"arxiv_1010.3882","title":"Introduction to papers on the modeling and analysis of network data","authors":[{"name":"Stephen E. Fienberg"}],"abstract":"Introduction to papers on the modeling and analysis of network data","source":"arXiv","year":2010,"language":"en","subjects":["stat.AP"],"doi":"10.1214/10-AOAS346","url":"https://arxiv.org/abs/1010.3882","pdf_url":"https://arxiv.org/pdf/1010.3882","is_open_access":true,"published_at":"2010-10-19T12:40:34Z","score":54},{"id":"arxiv_1010.3575","title":"Introducing the discussion paper by Székely and Rizzo","authors":[{"name":"Michael A. Newton"}],"abstract":"Introducing the discussion paper by Székely and Rizzo","source":"arXiv","year":2010,"language":"en","subjects":["stat.AP"],"doi":"10.1214/09-AOAS34INTRO","url":"https://arxiv.org/abs/1010.3575","pdf_url":"https://arxiv.org/pdf/1010.3575","is_open_access":true,"published_at":"2010-10-18T12:17:11Z","score":54},{"id":"crossref_10.1016/s1359-6349(10)71571-8","title":"775 Plant phenols modulate JNK activity in mouse epidermis: the effect on transcription factors AP-1 and STAT","authors":[{"name":"M. Cichocki"},{"name":"M. Dalek"},{"name":"W. Baer-Dubowska"}],"abstract":"","source":"CrossRef","year":2010,"language":"en","subjects":null,"doi":"10.1016/s1359-6349(10)71571-8","url":"https://doi.org/10.1016/s1359-6349(10)71571-8","is_open_access":true,"published_at":"","score":54},{"id":"arxiv_0804.0103","title":"Rejoinder of: Statistical analysis of an archeological find","authors":[{"name":"Andrey Feuerverger"}],"abstract":"Rejoinder of ``Statistical analysis of an archeological find'' [arXiv:0804.0079]","source":"arXiv","year":2008,"language":"en","subjects":["stat.AP"],"doi":"10.1214/08-AOAS99REJ","url":"https://arxiv.org/abs/0804.0103","pdf_url":"https://arxiv.org/pdf/0804.0103","is_open_access":true,"published_at":"2008-04-01T08:15:59Z","score":52}],"total":161256,"page":1,"page_size":20,"sources":["arXiv","DOAJ","CrossRef"],"query":"stat.AP"}