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新提交 (展示 1 之 1 条目 )

[1] arXiv:2508.03952 [中文pdf, pdf, 其他]

标题： 设计入门数据科学课程和教学法的蓝图 显示英文标题

标题： A Blueprint to Design Curriculum and Pedagogy for Introductory Data Science

Elijah Meyer, Mine Çetinkaya-Rundel

评论： 33页，4图

主题： 其他统计 (stat.OT)

随着数据科学领域工作需求的增加，大学开发和提供现代化的数据科学课程以培训学生从事这些工作的需求也在增加。 然而，这些课程的开发仍然具有挑战性，尤其是在入门级别。 为了帮助教师满足这一需求，我们提出了一种灵活的蓝图，支持开发现代化的入门数据科学课程。 这个蓝图通过在\university{}教授入门数据科学课程的视角和经验来叙述。 这是一门大型课程，面向STEM和非STEM专业的学生，并包括R、RStudio、Quarto、Git和GitHub等技术的整合和促进。 我们识别并讨论了教授现代化入门数据科学课程中的常见挑战，详细描述了一个学习模型，帮助学生逐步加深对数据科学概念的理解，并提供可重复使用的材料，以帮助教师在其大学采用和调整此类课程。 显示英文摘要

As the demand for jobs in data science increases, so does the demand for universities to develop and facilitate modernized data science curricula to train students for these positions. Yet, the development of these courses remains challenging, especially at the introductory level. To help instructors to meet this demand, we present a flexible blueprint that supports the development of a modernized introductory data science curriculum. This blueprint is narrated through the lens and experience in teaching the introductory data science course at \university{}. This is a large course that serves both STEM and non-STEM majors and includes the incorporation and facilitation of technologies such as R, RStudio, Quarto, Git, and GitHub. We identify and provide discussion around common challenges in teaching a modernized introductory data science course, detail a learning model for students to grow their understanding of data science concepts, and provide reproducible materials to help empower teachers to adopt and adapt such curriculum at their universities.

交叉提交 (展示 1 之 1 条目 )

[2] arXiv:2508.04080 (交叉列表自 cs.AI) [中文pdf, pdf, html, 其他]

标题： GeoSR：通过迭代自我完善探测地理空间知识边界的认知代理框架 显示英文标题

标题： GeoSR: Cognitive-Agentic Framework for Probing Geospatial Knowledge Boundaries via Iterative Self-Refinement

Jinfan Tang, Kunming Wu, Ruifeng Gongxie, Yuya He, Yuankai Wu

评论： 16页，9图

主题： 人工智能 (cs.AI) ; 其他统计 (stat.OT)

最近的研究已将大型语言模型（LLMs）的应用扩展到地理问题，揭示了即使没有明确的空间监督，也表现出令人惊讶的地理空间能力。 然而，LLMs在空间一致性、多跳推理和地理偏差方面仍面临挑战。 为了解决这些问题，我们提出了GeoSR，这是一种自我精炼的代理推理框架，将核心地理原理——尤其是托布勒地理学第一定律——嵌入到迭代预测循环中。 在GeoSR中，推理过程被分解为三个协作代理：(1) 一个变量选择代理，从同一位置选择相关协变量；(2) 一个点选择代理，选择之前轮次中由LLM生成的附近位置的参考预测；(3) 一个精炼代理，通过评估预测质量并在必要时触发进一步轮次来协调迭代精炼过程。 这种代理循环通过利用空间依赖性和变量间关系逐步提高预测质量。 我们在从现实世界属性估计到社会经济预测的任务上验证了GeoSR。 实验结果表明，与标准提示策略相比有持续改进，证明将地质统计先验和空间结构推理引入LLMs可以带来更准确和公平的地理空间预测。 GeoSR的代码可在 https://github.com/JinfanTang/GeoSR 获取。 显示英文摘要

Recent studies have extended the application of large language models (LLMs) to geographic problems, revealing surprising geospatial competence even without explicit spatial supervision. However, LLMs still face challenges in spatial consistency, multi-hop reasoning, and geographic bias. To address these issues, we propose GeoSR, a self-refining agentic reasoning framework that embeds core geographic principles -- most notably Tobler's First Law of Geography -- into an iterative prediction loop. In GeoSR, the reasoning process is decomposed into three collaborating agents: (1) a variable-selection agent that selects relevant covariates from the same location; (2) a point-selection agent that chooses reference predictions at nearby locations generated by the LLM in previous rounds; and (3) a refine agent that coordinates the iterative refinement process by evaluating prediction quality and triggering further rounds when necessary. This agentic loop progressively improves prediction quality by leveraging both spatial dependencies and inter-variable relationships. We validate GeoSR on tasks ranging from physical-world property estimation to socioeconomic prediction. Experimental results show consistent improvements over standard prompting strategies, demonstrating that incorporating geostatistical priors and spatially structured reasoning into LLMs leads to more accurate and equitable geospatial predictions. The code of GeoSR is available at https://github.com/JinfanTang/GeoSR.

替换提交 (展示 2 之 2 条目 )

[3] arXiv:2406.10612 (替换) [中文pdf, pdf, 其他]

标题： 使用概率模型和治疗选择标准在网络荟萃分析中生成治疗层次结构 显示英文标题

标题： Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria

Theodoros Evrenoglou, Adriani Nikolakopoulou, Guido Schwarzer, Gerta Rücker, Anna Chaimani

主题： 方法论 (stat.ME) ; 应用 (stat.AP) ; 其他统计 (stat.OT)

网络荟萃分析（NMA）的一个关键输出是治疗的相对排名；然而，它受到了大量批评。 现有的排名方法往往缺乏明确的可解释性，并且未能充分考虑不确定性，过分强调了治疗效果的小差异。 我们提出了一种新的框架，使用概率模型来估计NMA中的治疗层次，重点是临床相关的治疗选择标准（TCC）。 首先，我们制定一个数学表达式，基于最小有价值差异（SWD）来定义TCC，将NMA的相对治疗效果转换为治疗偏好格式。 然后，使用概率排名模型对这些数据进行综合，为每种治疗分配一个潜在的“能力”参数，表示其相对于网络中其他治疗产生临床重要且有益的真实治疗效果的倾向。 参数估计依赖于最大似然理论，标准误差从费舍尔信息矩阵中渐近得出。 为了便于使用我们的方法，我们发布了R包mtrank。 我们将该方法应用于两个临床数据集：一个比较18种抗抑郁药治疗重度抑郁症，另一个比较6种降压药对糖尿病发生率的影响。 我们的方法提供了稳健且可解释的治疗层次，考虑了具体的TCC。 我们进一步检查了所提出的方法与153个已发表网络中现有排名指标之间的一致性，得出结论：一致性的程度取决于NMA估计的精度。 我们的框架为NMA治疗排名提供了一个有价值的替代方案，减轻了对微小差异的过度解释。 这使得治疗层次更加可靠且具有临床意义。 显示英文摘要

A key output of network meta-analysis (NMA) is the relative ranking of treatments; nevertheless, it has attracted substantial criticism. Existing ranking methods often lack clear interpretability and fail to adequately account for uncertainty, over-emphasizing small differences in treatment effects. We propose a novel framework to estimate treatment hierarchies in NMA using a probabilistic model, focusing on a clinically relevant treatment-choice criterion (TCC). Initially, we formulate a mathematical expression to define a TCC based on smallest worthwhile differences (SWD), converting NMA relative treatment effects into treatment preference format. This data is then synthesized using a probabilistic ranking model, assigning each treatment a latent 'ability' parameter, representing its propensity to yield clinically important and beneficial true treatment effects relative to the rest of the treatments in the network. Parameter estimation relies on the maximum likelihood theory, with standard errors derived asymptotically from Fisher's information matrix. To facilitate the use of our methods, we launched the R package mtrank. We applied our method to two clinical datasets: one comparing 18 antidepressants for major depression and another comparing 6 antihypertensives for the incidence of diabetes. Our approach provided robust, interpretable treatment hierarchies that account for a concrete TCC. We further examined the agreement between the proposed method and existing ranking metrics in 153 published networks, concluding that the degree of agreement depends on the precision of the NMA estimates. Our framework offers a valuable alternative for NMA treatment ranking, mitigating over-interpretation of minor differences. This enables more reliable and clinically meaningful treatment hierarchies.

[4] arXiv:2409.14284 (替换) [中文pdf, pdf, html, 其他]

标题： 分布函数估计的调查数据集成 显示英文标题

标题： Survey Data Integration for Distribution Function Estimation

Jeremy Flood, Sayed Mostafa

主题： 统计理论 (math.ST) ; 应用 (stat.AP) ; 方法论 (stat.ME) ; 其他统计 (stat.OT)

将概率样本和非概率样本结合起来以估计有限总体总和（或均值）最近在抽样领域受到了广泛关注；然而，据我们所知，这一框架尚未扩展到累积分布函数（CDF）估计。 为弥补这一空白，我们提出了一种新的CDF估计器，该估计器结合了来自概率样本的数据和来自可能规模较大的非概率样本的数据。 假设在两者中都观察到了一组共享协变量，而响应变量仅在后者中被观察到，所提出的估计器使用基于便利样本训练的调查加权经验CDF的回归残差来估计响应变量的CDF。 在一些假设下，我们推导了我们的CDF估计器的渐近偏差和方差，并表明如果可忽略性成立，则它对于有限总体CDF是渐近无偏的。 我们的实证结果表明，在可忽略性下，所提出的CDF估计器对模型误设具有鲁棒性；在模型误设下，对可忽略性也具有鲁棒性；当两个假设都被违反时，我们的基于残差的CDF估计器仍然优于其“插件”质量填补和朴素的兄弟方法，尽管效率有所下降。 显示英文摘要

Integration of probabilistic and non-probabilistic samples for the estimation of finite population totals (or means) has recently received considerable attention in the field of survey sampling; yet, to the best of our knowledge, this framework has not been extended to cumulative distribution function (CDF) estimation. To address this gap, we propose a novel CDF estimator that integrates data from probability samples with data from, potentially big, nonprobability samples. Assuming that a set of shared covariates are observed in both, while the response variable is observed only in the latter, the proposed estimator uses a survey-weighted empirical CDF of regression residuals trained on the convenience sample to estimate the CDF of the response variable. Under some assumptions, we derive the asymptotic bias and variance of our CDF estimator and show that it is asymptotically unbiased for the finite population CDF if ignorability holds. Our empirical results imply that the proposed CDF estimator is robust to model misspecification under ignorability, and robust to ignorability under model misspecification; when both assumptions are violated, our residual-based CDF estimator still outperforms its `plug-in' mass imputation and naive siblings, albeit with noted decreases in efficiency.