Data Analysis with R

Latest papers

• A Stationary-Distribution Theory for Triplet-Based Plateau Search in Random Forest Ensemble-Size Selection

  Andrey A. Dukhovny, Andrey M. Lange · 1 July 2026

  The number of trees is a central computational parameter in Random Forests: increasing it reduces finite-ensemble variability but increases training and prediction cost. Plateau-based tuning adapts this parameter through local comparisons of out-of-bag scores at a geometric triplet of tree counts. A…

• Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis

  Minghao Fu, Biwei Huang, Zijian Li, Yujia Zheng, Ignavier Ng, Guangyi Chen, Yingyao Hu, Kun Zhang · 1 June 2026

  Understanding climate dynamics requires going beyond correlations in observational data to uncover the underlying causal process. Latent drivers such as atmospheric processes play a central role in temporal dynamics, while direct causal influences also exist among geographically proximate observed v…

• Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models

  Blas Kolic, Corrado Monti, Gianmarco De Francisci Morales, Marco Pangallo · 29 April 2026

  In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-Based Model (ABM). These models generate observable time series driven by evolving, partially-latent microstates. Latent states must be estimated to ali…

• Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond

  Minghao Liu, Zonglin Di, Jiaheng Wei, Zhongruo Wang, Hengxiang Zhang, Ruixuan Xiao, Haoyu Wang, Jinlong Pang, Hao Chen, Ankit Shah, Hongxin Wei, Xinlei He, Zhaowei Zhao, Haobo Wang, Lei Feng, Jindong Wang, James Davis, Yang Liu · 21 April 2026

  Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and cost…

• A Visualization for Comparative Analysis of Regression Models

  Nassime Mountasir (ICube), Baptiste Lafabregue (ICube), Bruno Albert (ICube), Nicolas Lachiche (ICube) · 23 March 2026

  As regression is a widely studied problem, many methods have been proposed to solve it, each of them often requiring setting different hyper-parameters. Therefore, selecting the proper method for a given application may be very difficult and relies on comparing their performances. Performance is usu…

• Exploring the Impact of Dataset Statistical Effect Size on Model Performance and Data Sample Size Sufficiency

  Arya Hatamian, Lionel Levine, Haniyeh Ehsani Oskouie, Majid Sarrafzadeh · 11 February 2026

  Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determine the adequacy of a dataset prior to training and evaluating a model's performance would be an essential tool for anyone engaged in experimental design …

• "Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training

  David L. Donoho, Jian Kang, Xihong Lin, Bhramar Mukherjee, Dan Nettleton, Rebecca Nugent, Abel Rodriguez, Eric P. Xing, Tian Zheng, Hongtu Zhu · 27 January 2026

  This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirica…

• Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood

  Byunghee Lee, Hye Yeon Sin, Joonsung Kang · 31 October 2025

  This study introduces an integrated framework for predictive causal inference designed to overcome limitations inherent in conventional single model approaches. Specifically, we combine a Hidden Markov Model (HMM) for spatial health state estimation with a Multi Task and Multi Graph Convolutional Ne…