Journal of the American Statistical Association最新文献

英文中文

Scalable Estimation of Multinomial Response Models with Random Consideration Sets 随机考虑集下多项响应模型的可扩展估计

IF 3.7 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association

Pub Date : 2026-01-07 DOI: 10.1080/01621459.2025.2609361

Siddhartha Chib, Kenichi Shimizu

引用次数: 0

A Latent Variable Approach to Learning High-dimensional Multivariate longitudinal Data 一种学习高维多元纵向数据的潜变量方法

IF 3.7 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association

Pub Date : 2026-01-07 DOI: 10.1080/01621459.2025.2606384

Sze Ming Lee, Yunxiao Chen, Tony Sit

引用次数: 0

Bayesian Image Analysis in Fourier Space. 傅里叶空间中的贝叶斯图像分析。

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association

Pub Date : 2026-01-05 DOI: 10.1080/01621459.2025.2573523

John Kornak, Karl Young, Eric Friedman, Konstantinos Bakas

Bayesian image analysis has been instrumental for over 40 years in addressing challenges such as image noise reduction, de-blurring, feature enhancement, and object detection. Despite its success, modeling spatial dependencies inherent to these problems often results in significant computational challenges. This work introduces the Bayesian Image Analysis in Fourier Space (BIFS) framework, which redefines conventional Bayesian modeling for continuous-valued images by transforming the problem into the Fourier domain. This transformation reduces the original high-dimensional dependent estimation problem into multiple low-dimensional, independent subproblems in Fourier space. The BIFS approach thereby simplifies computation while enabling flexible model specification, efficient formulation of isotropic priors, adaptability to diverse prior expectations, and invariance to changes in image resolution. BIFS thus offers a powerful and computationally efficient framework for a wide range of imaging applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

40多年来，贝叶斯图像分析在解决图像降噪、去模糊、特征增强和目标检测等挑战方面发挥了重要作用。尽管取得了成功，但这些问题固有的空间依赖性建模通常会导致重大的计算挑战。这项工作介绍了傅立叶空间中的贝叶斯图像分析（BIFS）框架，该框架通过将问题转换为傅立叶域，重新定义了连续值图像的传统贝叶斯建模。这种变换将原来的高维相关估计问题简化为傅里叶空间中的多个低维独立子问题。因此，BIFS方法简化了计算，同时实现了灵活的模型规范、有效的各向同性先验公式、对不同先验期望的适应性以及对图像分辨率变化的不变性。因此，BIFS为广泛的成像应用提供了一个强大且计算效率高的框架。本文的补充材料可在网上获得，包括可用于复制该作品的材料的标准化描述。

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引用次数: 0

Likelihood Methods in Survival Analysis: With R Examples 生存分析中的似然方法：附R例

IF 3.7 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association

Pub Date : 2026-01-05 DOI: 10.1080/01621459.2025.2605106

Lu Mao

引用次数: 0

Possibilistic inferential models: a review 可能性推理模型：综述

IF 3.7 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association

Pub Date : 2026-01-05 DOI: 10.1080/01621459.2025.2606127

Ryan Martin