Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Han, Ruijian"'
Autor:
Sun, Maojun, Han, Ruijian, Jiang, Binyan, Qi, Houduo, Sun, Defeng, Yuan, Yancheng, Huang, Jian
We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large models. LAMBDA is designed to address data analysis challenges in complex data-driven applications t
Externí odkaz:
http://arxiv.org/abs/2407.17535
We consider a covariate-assisted ranking model grounded in the Plackett--Luce framework. Unlike existing works focusing on pure covariates or individual effects with fixed covariates, our approach integrates individual effects with dynamic covariates
Externí odkaz:
http://arxiv.org/abs/2406.16507
Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This paper introduces a novel approach to online inference in high-dimensional gene
Externí odkaz:
http://arxiv.org/abs/2405.18284
Pairwise comparison models have been widely used for utility evaluation and ranking across various fields. The increasing scale of problems today underscores the need to understand statistical inference in these models when the number of subjects div
Externí odkaz:
http://arxiv.org/abs/2401.08463
Autor:
Han, Ruijian, Xu, Yiming
The Plackett--Luce model has been extensively used for rank aggregation in social choice theory. A central question in this model concerns estimating the utility vector that governs the model's likelihood. In this paper, we investigate the asymptotic
Externí odkaz:
http://arxiv.org/abs/2306.02821
Forward simulation-based uncertainty quantification that studies the distribution of quantities of interest (QoI) is a crucial component for computationally robust engineering design and prediction. There is a large body of literature devoted to accu
Externí odkaz:
http://arxiv.org/abs/2303.06422
Archetypal analysis is an unsupervised learning method for exploratory data analysis. One major challenge that limits the applicability of archetypal analysis in practice is the inherent computational complexity of the existing algorithms. In this pa
Externí odkaz:
http://arxiv.org/abs/2108.05767
In this paper we develop an online statistical inference approach for high-dimensional generalized linear models with streaming data for real-time estimation and inference. We propose an online debiased lasso (ODL) method to accommodate the special s
Externí odkaz:
http://arxiv.org/abs/2108.04437
We propose an online debiased lasso (ODL) method for statistical inference in high-dimensional linear models with streaming data. The proposed ODL consists of an efficient computational algorithm for streaming data and approximately normal estimators
Externí odkaz:
http://arxiv.org/abs/2106.05925
Statistical inference using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. In this paper, we propose a general framework to model the mutual interactions in a network, which enjoys ample flexibility in ter
Externí odkaz:
http://arxiv.org/abs/2002.08853