Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.

Hee Cheol Chung, Yang Ni, Irina Gaynanova

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Truncated Gaussian copula principal component analysis with application to pediatric acute lymphoblastic leukemia patients' gut microbiome.Statistical methods in medical research · 2026
Article

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Hee Cheol ChungDepartment of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.

Yang NiDepartment of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX 78705, USA.

Irina GaynanovaDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data (R01GM148974)R01GM148974 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI NI, YANG · 2022 to 2025

$1.2M

NIGMS NIH HHS R01 GM148974

6 · The paper itself

Abstract

Sequencing-based technologies provide an abundance of high-dimensional biological data sets with highly skewed and zero-inflated measurements. Despite the computational efficiency and high interpretability offered by linear classification methods, the violation of underlying distribution assumptions, driven by high skewness and zero inflation, results in invalid classification rules and interpretations. Furthermore, existing data transformation methods addressing these violations introduce ambiguity, rendering the final model and classification performance contingent on the specific transformation employed. To tackle these challenges, we propose a novel semiparametric framework for discriminant analysis based on the truncated latent Gaussian copula model. This model accommodates skewness and zero inflation, and its estimation procedure ensures robustness against data transformations. To facilitate model interpretability, we incorporate

Indexed as

Latent Gaussian copulaprobit regressionrobust classificationsequencing dataskewed datavariable selection

Identifiers

PMID42325817

PMCPMC13278561

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.