arbiter
JournalsAccount

Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes

Shonosuke Sugasawa et al.

Journal of Multivariate Analysis2026https://doi.org/10.1016/j.jmva.2026.105635article
AJG 3ABDC A
Weight
0.50

Abstract

No abstract available.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.jmva.2026.105635

Or copy a formatted citation

@article{shonosuke2026,
  title        = {{Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes}},
  author       = {Shonosuke Sugasawa et al.},
  journal      = {Journal of Multivariate Analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jmva.2026.105635},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.