ArticleBiometrics2010
Pairwise variable selection for high-dimensional model-based clustering.
Article in Biometrics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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.
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Who cites it
12 citing papers in PubMed.
- Regression Trees With Fused Leaves.Statistics in medicine · 2024Article
- Heterogeneous Functional Regression for Subgroup Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.Genome research · 2024Article
- Nonparametric prediction distribution from resolution-wise regression with heterogeneous data.Journal of business & economic statistics : a publication of the American Statistical Association · 2023Article
- A Hyperparameter-Free, Fast and Efficient Framework to Detect Clusters From Limited Samples Based on Ultra High-Dimensional Features.IEEE access : practical innovations, open solutions · 2022Article
- Identifying Heterogeneous Effect using Latent Supervised Clustering with Adaptive Fusion.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Clustering High-Dimensional Landmark-based Two-dimensional Shape DataJournal of the American Statistical Association · 2015Article
- Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering.Journal de la Societe francaise de statistique (2009) · 2014Article
- Supervised Bayesian latent class models for high-dimensional data.Statistics in medicine · 2012Article
- Penalized model-based clustering with unconstrained covariance matrices.Electronic journal of statistics · 2009Article
- Integrative clustering methods for multi-omics data.Wiley interdisciplinary reviews. Computational statisticsArticle
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Variable selection for clustering is an important and challenging problem in high-dimensional data analysis. Existing variable selection methods for model-based clustering select informative variables in a "one-in-all-out" manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high-dimensional model-based clustering. The method is based on a new pairwise penalty. Results on simulated and real data show that the new method performs better than alternative approaches that use ℓ(1) and ℓ(∞) penalties and offers better interpretation.
Indexed as
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What OpenQuestion holds
Registered trials
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.