Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Sang, Peijun"'
In diagnostic studies, researchers frequently encounter imperfect reference standards with some misclassified labels. Treating these as gold standards can bias receiver operating characteristic (ROC) curve analysis. To address this issue, we propose
Externí odkaz:
http://arxiv.org/abs/2502.08569
We propose multiplier bootstrap procedures for nonparametric inference and uncertainty quantification of the target mean function, based on a novel framework of integrating target and source data. We begin with the relatively easier covariate shift s
Externí odkaz:
http://arxiv.org/abs/2501.01610
We propose a functional stochastic block model whose vertices involve functional data information. This new model extends the classic stochastic block model with vector-valued nodal information, and finds applications in real-world networks whose nod
Externí odkaz:
http://arxiv.org/abs/2407.00564
We propose a novel test procedure for comparing mean functions across two groups within the reproducing kernel Hilbert space (RKHS) framework. Our proposed method is adept at handling sparsely and irregularly sampled functional data when observation
Externí odkaz:
http://arxiv.org/abs/2312.07727
The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matri
Externí odkaz:
http://arxiv.org/abs/2310.10952
Inferring the parameters of ordinary differential equations (ODEs) from noisy observations is an important problem in many scientific fields. Currently, most parameter estimation methods that bypass numerical integration tend to rely on basis functio
Externí odkaz:
http://arxiv.org/abs/2304.02127
Traditional static functional data analysis is facing new challenges due to streaming data, where data constantly flow in. A major challenge is that storing such an ever-increasing amount of data in memory is nearly impossible. In addition, existing
Externí odkaz:
http://arxiv.org/abs/2302.02457
Publikováno v:
Journal of Agricultural, Biological & Environmental Statistics (JABES). Dec2024, Vol. 29 Issue 4, p853-873. 21p.
We establish nonparametric identification of auction models with continuous and nonseparable unobserved heterogeneity using three consecutive order statistics of bids. We then propose sieve maximum likelihood estimators for the joint distribution of
Externí odkaz:
http://arxiv.org/abs/2210.03547
Autor:
Sang, Peijun, Li, Bing
We propose a nonlinear function-on-function regression model where both the covariate and the response are random functions. The nonlinear regression is carried out in two steps: we first construct Hilbert spaces to accommodate the functional covaria
Externí odkaz:
http://arxiv.org/abs/2207.08211