Covariance-enhanced discriminant analysis.

Peirong Xu, J I Zhu, Lixing Zhu, Y I Li

Abstract read

In one paragraph

Article in Biometrika. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Dynamic Supervised Principal Component Analysis for Classification.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025
Article
Multiclass linear discriminant analysis with ultrahigh-dimensional features.Biometrics · 2019
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Peirong XuDepartment of Mathematics, Southeast University, Nanjing 211189, China.

J I ZhuDepartment of Statistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.

Lixing ZhuDepartment of Mathematics, Hong Kong Baptist University, Hong Kong, China.

Y I LiDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.

Funding

Sparse Structure Identification from High-Dimensional Epigenomic DataR01GM096194 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZHU, JI · 2010 to 2013

$1.0M

NIGMS NIH HHS R01 GM096194

6 · The paper itself

Abstract

Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. Under mild regularity conditions, we show that the method can achieve consistent parameter estimation and model selection, and can attain an asymptotically optimal misclassification rate. Extensive simulations have verified the utility of the method, which we apply to a renal transplantation trial.

Indexed as

CorrelationGraphical lassoLinear discriminant analysisPairwise fusionVariable selection

Identifiers

PMID27330194

DOI10.1093/biomet/asu049

PMCPMC4912060

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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.