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Zuofeng Shang

46 papers hereh-index 17865 citations81 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author9
  • middle author12
  • last author25

Across the 46 of 46 papers where every author was matched, so the position is known.

fields
  • math.ST22
  • stat.ME11
  • stat.ML8
  • astro-ph.SR1
  • cs.IT1
  • cs.LG1
same name
  • Zuofeng Shang — 2 papers, h 1
  • Zuofeng Shang — 1 paper
  • Zuofeng Shang — 1 paper, h 1
  • Zuofeng Shang — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112026
most citedLocal and global asymptotic inference in smoothing spline models

60 citations · 266 across the 46 of their papers we have counts for

collaborators
Showing 2013Show all

4 papers · 1 filter

stat.ME2013

Sparse and Efficient Estimation for Partial Spline Models with Increasing Dimension

Guang Cheng, Hao Helen Zhang, Zuofeng Shang

We consider model selection and estimation for partial spline models and propose a new regularization method in the context of smoothing splines. The regularization method has a si…

math.ST2013★ 28 cited

Joint asymptotics for semi-nonparametric regression models with partially linear structure

Guang Cheng, Zuofeng Shang

We consider a joint asymptotic framework for studying semi-nonparametric regression models where (finite-dimensional) Euclidean parameters and (infinite-dimensional) functional par…

stat.ME2013

High-Dimensional Bayesian Inference in Nonparametric Additive Models

Zuofeng Shang, Ping Li

A fully Bayesian approach is proposed for ultrahigh-dimensional nonparametric additive models in which the number of additive components may be larger than the sample size, though…

stat.ME2013★ 2 cited

Bayesian Ultrahigh-Dimensional Screening Via MCMC

Zuofeng Shang, Ping Li

We explore the theoretical and numerical property of a fully Bayesian model selection method in sparse ultrahigh-dimensional settings, i.e., p≫n, where p is the number of co…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.