Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Sang, Peijun"'
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
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
Autor:
Luo, Yao, Sang, Peijun
Estimating structural models is an essential tool for economists. However, existing methods are often inefficient either computationally or statistically, depending on how equilibrium conditions are imposed. We propose a class of penalized sieve esti
Externí odkaz:
http://arxiv.org/abs/2204.13488
Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serves as important building blocks for forecasting and model building. Decades of research have advanced methods fo
Externí odkaz:
http://arxiv.org/abs/2203.14760
We propose inferential tools for functional linear quantile regression where the conditional quantile of a scalar response is assumed to be a linear functional of a functional covariate. In contrast to conventional approaches, we employ kernel convol
Externí odkaz:
http://arxiv.org/abs/2202.11747