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Jing Ouyang

10 papers hereh-index 115 citations9 works total

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

author position
  • first author4
  • middle author5

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • stat.ME9
  • math.ST1
same name
  • Jing Ouyang — 4 papers, h 3
  • Jing Ouyang — 3 papers, h 1
  • Jing Ouyang — 1 paper
  • Jing Ouyang — 1 paper, 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
20212026
most citedLearning Latent and Hierarchical Structures in Cognitive Diagnosis Models

34 citations · 76 across the 10 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

stat.ME2021★ 24 cited

DIF Statistical Inference without Knowing Anchoring Items

Yunxiao Chen, Chengcheng Li, Jing Ouyang +1

Establishing the invariance property of an instrument is a key step for establishing its measurement validity. Measurement invariance is typically assessed by differential item fun…

math.ST2021★ 1 cited

Statistical Inference for Noisy Incomplete Binary Matrix

Yunxiao Chen, Chengcheng Li, Jing Ouyang +1

We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categori…

stat.ME2021★ 34 cited

Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models

Chenchen Ma, Gongjun Xu

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models that are widely used in modern educational, psychological, social and biological sciences.…

stat.ME2021★ 8 cited

Identifiability of Latent Class Models with Covariates

Jing Ouyang, Gongjun Xu

Latent class models with covariates are widely used for psychological, social, and educational research. Yet the fundamental identifiability issue of these models has not been full…

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