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Haolei Weng

19 papers hereh-index 10287 citations32 works total

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

author position
  • first author5
  • middle author10
  • last author4

Across the 19 of 19 papers where every author was matched, so the position is known.

fields
  • math.ST9
  • stat.ME4
  • stat.ML4
  • cs.IT1
  • stat.CO1
same name
  • Haolei Weng — 3 papers, h 1
  • Haolei Weng — 3 papers, h 0

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
20132025
most citedCommunity detection with nodal information

14 citations · 63 across the 16 of their papers we have counts for

collaborators
Showing stat.MEShow all

4 papers · 1 filter

stat.ME2023

Post-Selection Inference for the Cox Model with Interval-Censored Data

Jianrui Zhang, Chenxi Li, Haolei Weng

We develop a post-selection inference method for the Cox proportional hazards model with interval-censored data, which provides asymptotically valid p-values and confidence interva…

stat.ME2022★ 1 cited

Variable Selection in Latent Regression IRT Models via Knockoffs: An Application to International Large-scale Assessment in Education

Zilong Xie, Yunxiao Chen, Matthias von Davier +1

International large-scale assessments (ILSAs) play an important role in educational research and policy making. They collect valuable data on education quality and performance deve…

stat.ME2016★ 14 cited

Community detection with nodal information

Haolei Weng, Yang Feng

Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the…

stat.ME2013★ 4 cited

Regularization after retention in ultrahigh dimensional linear regression models

Haolei Weng, Yang Feng, Xingye Qiao

In ultrahigh dimensional setting, independence screening has been both theoretically and empirically proved a useful variable selection framework with low computation cost. In this…

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